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Architecture

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Andrew J. Ko
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Once you have a sense of what your design must do (in the form of requirements or other less formal specifications), the next big problem is one of organization. How will you order all of the different data, algorithms, and control implied by your requirements? With a small program of a few hundred lines, you can get away without much organization, but as programs scale, they quickly become impossible to manage alone, let alone with multiple developers. Much of this challenge occurs because requirements change, and every time they do, code has to change to accommodate. The more code their is and the more entangled it is, the harder it is to change and more likely you are to break things.

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This is where architecture comes in. Architecture is a way of organizing code, just like building architecture is a way of organizing space. The idea of software architecture has at its foundation a principle of information hiding: the less a part of a program knows about other parts of a program, the easier it is to change. The most popular information hiding strategy is encapsulation: this is the idea of designing self-contained abstractions with well-defined interfaces that separate different concerns in a program. Programming languages offer encapsulation support through things like functions and classes, which encapsulate data and functionality together. Another programming language encapsulation method is scoping, which hides variables and other names from other parts of program outside a scope. All of these strategies attempt to encourage developers to maximize information hiding and separation of concerns. If you get your encapsulation right, you should be able to easily make changes to a program's behavior without having to change everything about it's implementation.

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When encapsulation strategies fail, one can end up with what some affectionately call a "ball of mud" architecture or "spaghetti code". A more precise concept is cross-cutting concerns, which are things like features and functionality that span multiple different components of a system, or even an entire system. There is some evidence that cross-cutting concerns can lead to difficulties in program comprehension and long-term design degradation (Walker et al. 2012), all of which reduce productivity and increase the risk of defects. As long-lived systems get harder to change, they can take on technical debt, which is the degree to which an implementation is out of sync with a team's understanding of what a product is intended to be. Many developers view such debt as emerging from primarily from poor architectural decisions (Ernst et al. 2015). Over time, this debt can further result in organizational challenges (Khadka et al. 2014), making change even more difficult.

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The preventative solution to this problems is to try to design architecture up front, mitigating the various risks that come from cross-cutting concerns (defects, low modifiability, etc.) (Fairbanks 2010). A popular method in the 1990's was the Unified Modeling Language (UML), which was a series of notations for expressing the architectural design of a system before implementing it. Recent studies show that UML generally not used and generally not universal (Petre 2013). More recent developers have investigated ideas of architectural styles, which are patterns of interactions and information exchange between encapsulated components. Some common architectural styles include:

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Architectural styles come in all shapes and sizes. Some are smaller design patterns of information sharing (Beck et al. 2006), whereas others are ubiquitous but specialized patterns such as the architectures required to support undo and cancel in user interfaces (Bass et al. 2004).

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One fundamental unit of which an architecture is composed is a component. This is basically a word that refers to any abstraction—any code, really—that attempts to encapsulate some well defined functionality or behavior separate from other functionality and behavior. Components have interfaces that decide how it can communicate with other components. It might be a class, a data structure, a set of functions, a library, or even something like a web service. All of these are abstractions that encapsulate interrelated computation and state. The second fundamental unit of architecture is connectors. Connectors are abstractions (code) that transmit information between components. They're brokers that connect components, but do not necessarily have meaningful computation or state of their own. Connectors can be things like function calls, web service API calls, events, requests, and so on.

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Even with carefully selected architectures, systems can still be difficult to put together, leading to architectural mismatch (Garlan et al. 1995). When mismatch occurs, connecting two styles can require dramatic amounts of code to connect, imposing significant risk of defects and cost of maintenance. One common example of mismatches occurs with the ubiquitous use of database schemas with client/server web-applications. A single change in a database schema can often result in dramatic changes in an application, as every line of code that uses that part of the scheme either directly or indirectly must be updated (Qiu et al. 2013). This kind of mismatch occurs because the component that manages data (the database) and the component that renders data (the user interface) is highly "coupled" with the database schema: the user interface needs to know a lot about the data, its meaning, and its structure in order to render it meaningfully.

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The most common approach to dealing with both architectural mismatch and the changing of requirements over time is refactoring, which means changing the architecture of an implementation without changing its behavior. Refactoring is something most developers do as part of changing a system (Murphy-Hill et al 2009, Silva et al. 2016). Refactoring code to eliminate mismatch and technical debt can simplify change in the future, saving time (Ng et al. 2006) and prevent future defects (Kim et al. 2012). + +

Next chapter: Specifications
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Further reading

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Len Bass, Bonnie E. John, Natalia Juristo, and Maria-Isabel Sanchez-Segura. 2004. Usability-Supporting Architectural Patterns. In Proceedings of the 26th International Conference on Software Engineering (ICSE '04). IEEE Computer Society, Washington, DC, USA, 716-717.

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Kent Beck, Ron Crocker, Gerard Meszaros, John Vlissides, James O. Coplien, Lutz Dominick, and Frances Paulisch. 1996. Industrial experience with design patterns. In Proceedings of the 18th international conference on Software engineering (ICSE '96). IEEE Computer Society, Washington, DC, USA, 103-114.

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Jürgen Cito, Philipp Leitner, Thomas Fritz, and Harald C. Gall. 2015. The making of cloud applications: an empirical study on software development for the cloud. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2015). ACM, New York, NY, USA, 393-403.

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Neil A. Ernst, Stephany Bellomo, Ipek Ozkaya, Robert L. Nord, and Ian Gorton. 2015. Measure it? Manage it? Ignore it? Software practitioners and technical debt. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2015). ACM, New York, NY, USA, 50-60.

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Fairbanks, G. (2010). Just enough software architecture: a risk-driven approach. Marshall & Brainerd.

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Garlan, D., Allen, R., & Ockerbloom, J. (1995). Architectural mismatch or why it's hard to build systems out of existing parts. In Proceedings of the 17th international conference on Software engineering (pp. 179-185).

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Ravi Khadka, Belfrit V. Batlajery, Amir M. Saeidi, Slinger Jansen, and Jurriaan Hage. 2014. How do professionals perceive legacy systems and software modernization? In Proceedings of the 36th International Conference on Software Engineering (ICSE 2014). ACM, New York, NY, USA, 36-47.

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Miryung Kim, Thomas Zimmermann, and Nachiappan Nagappan. 2012. A field study of refactoring challenges and benefits. In Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering (FSE '12). ACM, New York, NY, USA, , Article 50 , 11 pages.

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Emerson Murphy-Hill, Chris Parnin, and Andrew P. Black. 2009. How we refactor, and how we know it. In Proceedings of the 31st International Conference on Software Engineering (ICSE '09). IEEE Computer Society, Washington, DC, USA, 287-297.

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T. H. Ng, S. C. Cheung, W. K. Chan, and Y. T. Yu. 2006. Work experience versus refactoring to design patterns: a controlled experiment. In Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering (SIGSOFT '06/FSE-14). ACM, New York, NY, USA, 12-22.

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Marian Petre. 2013. UML in practice. In Proceedings of the 2013 International Conference on Software Engineering (ICSE '13). IEEE Press, Piscataway, NJ, USA, 722-731.

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Danilo Silva, Nikolaos Tsantalis, and Marco Tulio Valente. 2016. Why we refactor? Confessions of GitHub contributors. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2016). ACM, New York, NY, USA, 858-870.

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Dong Qiu, Bixin Li, and Zhendong Su. 2013. An empirical analysis of the co-evolution of schema and code in database applications. In Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2013). ACM, New York, NY, USA, 125-135.

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Robert J. Walker, Shreya Rawal, and Jonathan Sillito. 2012. Do crosscutting concerns cause modularity problems? In Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering (FSE '12). ACM, New York, NY, USA, , Article 49 , 11 pages.

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Podcasts

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Software Engineering Daily, React JS with Sebastian Marbage and Christopher Chedeua

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Communication

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Andrew J. Ko
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Because software engineering often times distributes work across multiple people, a fundamental challenge in software engineering is ensuring that everyone on a team has the same understanding of what is being built and why. In the seminal book "The Mythical Man Month", Fred Brooks argued that good software needs to have conceptual integrity, both in how it is designed, but also how it is implemented (Brooks 1995). When multiple people are responsible for implementing a single coherent idea, how can they ensure they are building the same idea?

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The solution is effective communication. When communication is poor and teams are disconnected, software defects are the result (Bettenburg & Hassan 2013). The social relationships in a team also play a large role in structuring how projects evolve (Zhou & Mockus 2011). Perhaps the most notable theory underlying these ideas is Conway's Law (Conway 1968), which argues that any designed system—software included—will reflect the social relationships behind it's design. For example, look at any online banking website: the way the application is designed, how information is organized, the terminology that is used, and even the visual design is a reflection of how the teams inside that bank are organized and socially connected.

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Because communication is so central, software engineers are constantly seeking information to further their work, going to their coworkers' desks, emailing them, chatting via messaging platforms, and even using social media (Ko et al. 2007). Some of the information that developers are seeking is easier to find than others. For example, in the study I just cited, it was pretty trivial to find information about how wrote a line of code or whether a build was done, but when the information they needed resided in someone else's head (e.g., why a particular line of code was written), it was slow or often impossible to retrieve it. Sometimes it's not even possible to find out who has the information. Researchers have investigated tools for trying to quantify expertise by automatically analyzing the code that developers have written, building platforms to help developers search for other developers who might know what they need to know (Mockus & Herbsleb 2002, Begel et al. 2010).

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Communication is not always effective. In fact, there are many kinds of communication that are highly problematic in software engineering teams. For example, Perlow (1999) conducted an ethnography of one team and found a highly dysfunctional use of interruptions in which the most expert members of a team were constantly interrupted to "fight fires" (immediately address critical problems) in other parts of the organization, and then the organization rewarded them for their heroics. This not only made the most expert engineers less productive, but it also disincentivized the rest of the organization to find effective ways of preventing the disasters from occurring in the first place. Not all interruptions are bad, and they can increase productivity, but they do increase stress (Mark et al. 2008).

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Communication isn't just about transmitting information; it's also about relationships and identity. For example, the dominant culture of many software engineering work environments—and even the perceived culture—is one that can deter many people from even pursuing careers in computer science. Modern work environments are still dominated by men, who speak loudly, out of turn, and disrespectfully, with some even bordering on sexual harassment. These are not the conditions for trusting, effective communication.

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When communication is effective, it still takes time. One of the key strategies for reducing the amount of communication necessary is knowledge sharing tools, which broadly refers to any information system that stores facts that developers would normally have to retrieve from a person. By storing them in a database and making them easy to search, teams can avoid interruptions. The most common knowledge sharing tools in software teams are issue trackers, which are often at the center of communication not only between developers, but also with every other part of a software organization (Bertram et al. 2010). Community portals, such as GitHub pages or Slack teams, can also be effective ways of sharing documents and archiving decisions (Treude & Storey 2011). Perhaps the most popular knowledge sharing tool in software engineering today is Stack Overflow, which archives facts about programming language and API usage.

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Because all of this knowledge is so critical to progress, when developers leave an organization and haven't archived their knowledge somewhere, it can be quite disruptive to progress. Organizations often have single points of failure, in which a single developer may be critical to a team's ability to maintain and enhance a software product (Rigby et al. 2016). When newcomers join a team and lack the right knowledge, they introduce defects (Foucault et al. 2015). Some companies try to mitigate this by rotating developers between projects, "cross-training" them to ensure that the necessary knowledge to maintain a project is distributed across multiple engineers.

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What does all of this mean for you as an individual developer? To put it simply, don't underestimate the importance of talking. Know who you need to talk to, talk to them frequently, and to the extent that you can, write down what you know both to lessen the demand for talking and mitigate the risk of you not being available, but also to make your knowledge more precise and accessible in the future. It often takes decades for engineers to excel at communication. The very fact that you know why communication is important gives you an critical head start.

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Next chapter: Productivity
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Further reading

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Salah Bendifallah and Walt Scacchi. 1989. Work structures and shifts: an empirical analysis of software specification teamwork. In Proceedings of the 11th international conference on Software engineering (ICSE '89). ACM, New York, NY, USA, 260-270.

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Andrew Begel, Yit Phang Khoo, and Thomas Zimmermann. 2010. Codebook: discovering and exploiting relationships in software repositories. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1 (ICSE '10), Vol. 1. ACM, New York, NY, USA, 125-134.

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Dane Bertram, Amy Voida, Saul Greenberg, and Robert Walker. 2010. Communication, collaboration, and bugs: the social nature of issue tracking in small, collocated teams. In Proceedings of the 2010 ACM conference on Computer supported cooperative work (CSCW '10). ACM, New York, NY, USA, 291-300.

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Bettenburg, N., & Hassan, A. E. (2013). Studying the impact of social interactions on software quality. Empirical Software Engineering, 18(2), 375-431.

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Brooks, F.B. (1995). The Mythical Man-Month: Essays on Software Engineering, Addison-Wesley.

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Conway, M. E. (1968). How do committees invent. Datamation, 14(4), 28-31.

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Torgeir Dingsøyr and Emil Røyrvik. 2003. An empirical study of an informal knowledge repository in a medium-sized software consulting company. In Proceedings of the 25th International Conference on Software Engineering (ICSE '03). IEEE Computer Society, Washington, DC, USA, 84-92.

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Matthieu Foucault, Marc Palyart, Xavier Blanc, Gail C. Murphy, and Jean-Rémy Falleri. 2015. Impact of developer turnover on quality in open-source software. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2015). ACM, New York, NY, USA, 829-841.

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Andrew J. Ko, Robert DeLine, and Gina Venolia. 2007. Information Needs in Collocated Software Development Teams. In Proceedings of the 29th international conference on Software Engineering (ICSE '07). IEEE Computer Society, Washington, DC, USA, 344-353.

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Mark, G., Gudith, D., & Klocke, U. (2008, April). The cost of interrupted work: more speed and stress. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems (pp. 107-110).

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Audris Mockus. 2010. Organizational volatility and its effects on software defects. In Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering (FSE '10). ACM, New York, NY, USA, 117-126.

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Audris Mockus and James D. Herbsleb. 2002. Expertise browser: a quantitative approach to identifying expertise. In Proceedings of the 24th International Conference on Software Engineering (ICSE '02). ACM, New York, NY, USA, 503-512.

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Perlow, L. A. (1999). The time famine: Toward a sociology of work time. Administrative science quarterly, 44(1), 57-81.

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Pikkarainen, M., Haikara, J., Salo, O., Abrahamsson, P., & Still, J. (2008). The impact of agile practices on communication in software development. Empirical Software Engineering, 13(3), 303-337.

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Peter C. Rigby, Yue Cai Zhu, Samuel M. Donadelli, and Audris Mockus. 2016. Quantifying and mitigating turnover-induced knowledge loss: case studies of chrome and a project at Avaya. In Proceedings of the 38th International Conference on Software Engineering (ICSE '16). ACM, New York, NY, USA, 1006-1016.

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Ronnie E. S. Santos, Fabio Q. B. da Silva, Cleyton V. C. de Magalhães, and Cleviton V. F. Monteiro. 2016. Building a theory of job rotation in software engineering from an instrumental case study. In Proceedings of the 38th International Conference on Software Engineering (ICSE '16). ACM, New York, NY, USA, 971-981.

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Sfetsos, P., Stamelos, I., Angelis, L., & Deligiannis, I. (2009). An experimental investigation of personality types impact on pair effectiveness in pair programming. Empirical Software Engineering, 14(2), 187.

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Christoph Treude and Margaret-Anne Storey. 2011. Effective communication of software development knowledge through community portals. In Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering (ESEC/FSE '11). ACM, New York, NY, USA, 91-101.

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Christoph Treude and Margaret-Anne Storey. 2009. How tagging helps bridge the gap between social and technical aspects in software development. In Proceedings of the 31st International Conference on Software Engineering (ICSE '09). IEEE Computer Society, Washington, DC, USA, 12-22.

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Keiji Uemura and Miki Ohori. 1984. A cooperative approach to software development by application engineers and software engineers. In Proceedings of the 7th international conference on Software engineering (ICSE '84). IEEE Press, Piscataway, NJ, USA, 86-96.

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Minghui Zhou and Audris Mockus. 2011. Does the initial environment impact the future of developers? In Proceedings of the 33rd International Conference on Software Engineering (ICSE '11). ACM, New York, NY, USA, 271-280.

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Podcasts

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Software Engineering Daily. Female Pursuit of Computer Science with Jennifer Wang.

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Software Engineering Daily. The State of Programming with Stack Overflow Co-Founder Jeff Atwood.

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Program Comprehension

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Andrew J. Ko
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Despite all of the different activities that we've talked about so far—communicating, coordinating, planning, designing, architecting—really, most of a software engineers time is spent reading code (Maalej et al. 2014). Sometimes this is their own code, which makes this reading easier. Most of the time, it is someone else's code, whether it's a teammate's, or part of a library or API you're using. We call this reading program comprehension.

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Being good at program comprehension is a critical skill. You need to be able to read a function and know what it will do with its inputs; you need to be able to read a class and understand its state and functionality; you also need to be able to comprehend a whole implementation, understanding its architecture. Without these skills, you can't test well, you can't debug well, and you can fix or enhance the systems you're building or maintaining. In fact, studies of software engineers' first year at their first job show that a significant majority of their time is spent trying to simply comprehend the architecture of the system they are building or maintaining and understanding the processes that are being followed to modify and enhance them (Dagenais et al. 2010).

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What's going on when developers comprehend code? Usually, developers are trying to answer questions about code that help them build larger models of how a program works. Because program comprehension is hard, they avoid it when they can, relying on explanations from other developers rather than trying to build precise models of how a program works on their own (Roehm et al. 2012). When they do try to comprehend code, developers are usually trying to answer questions. Several studies have many general questions that developers must be able to answer in order to understand programs (Sillito et al. 2006, LaToza & Myers 2010). Here are over forty common questions that developers ask:

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Which type represents this domain concept or this UI element or action?Where in the code is the text in this error message or UI element?
Where is there any code involved in the implementation of this behavior?Is there an entity named something like this in that unit (for example in a project, package or class)?
What are the parts of this type?Which types is this type a part of?
Where does this type fit in the type hierarchy?Does this type have any siblings in the type hierarchy?
Where is this field declared in the type hierarchy?Who implements this interface or these abstract methods?
Where is this method called or type referenced?When during the execution is this method called?
Where are instances of this class created?Where is this variable or data structure being accessed?
What data can we access from this object?What does the declaration or definition of this look like?
What are the arguments to this function?What are the values of these arguments at runtime?
What data is being modified in this code?How are instances of these types created and assembled?
How are these types or objects related?How is this feature or concern (object ownership, UI control, etc) implemented?
What in this structure distinguishes these cases?What is the "correct" way to use or access this data structure?
How does this data structure look at runtime?How can data be passed to (or accessed at) this point in the code?
How is control getting (from here to) here?Why isn't control reaching this point in the code?
Which execution path is being taken in this case?Under what circumstances is this method called or exception thrown?
What parts of this data structure are accessed in this code?How does the system behavior vary over these types or cases?
What are the differences between these files or types?What is the difference between these similar parts of the code (e.g., between sets of methods)?
What is the mapping between these UI types and these model types?How can we know this object has been created and initialized correctly?
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If you think about the massive diversity in this list, you can see why program comprehension requires expertise. You not only need to understand programming languages quite well, but you also need to have strategies for answering all of the questions above (and more) quickly, effectively, and accurately.

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So how do developers go about answering these questions? Studies comparing experts and novices show that experts use prior knowledge that they have about architecture, design patterns, and the problem domain a program is built for to know what questions to ask and how to answer them, whereas novices use surface features of code, which leads them to spend considerable time reading code that is irrelevant to a question ((von Mayrhauser & Vans 1994), LaToza et al. 2007). Fundamentally, reading and comprehending source code is fundamentally different from those of reading and comprehending natural language (Binkley et al. 2013); what experts are doing is ultimately reasoning about dependencies between code (Weiser 1981). Dependencies include things like data dependencies (where a variable is used to compute something, what modifies a data structure, how data flows through a program, etc.) and control dependencies (which components call which functions, which events can trigger a function to be called, how a function is reached, etc.). All of the questions above fundamentally get at different types of data and control dependencies. In fact, theories of how developers navigate code by following these dependencies are highly predictive of what information a developer will seek next (Fleming et al. 2013), suggesting that expert behavior is highly routine.

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While much of program comprehension is skill, some of it is determined by design. For example, some programming languages result in programs that are more comprehensible. One framework called the Cognitive Dimensions of Notations (Green 1989) lays out some of the tradeoffs in programming language design that result in these differences in comprehensibility. For example, one of the dimensions in the framework is consistency, which refers to how much of a notation can be guessed based on an initial understanding of a language. JavaScript is a low-consistency language because of operators like ==, which behave differently depending on what the type of the left and right operands are. Knowing the behavior for Booleans doesn't tell you the behavior for a Boolean being compared to an integer. In contrast, Java is a high consistency language: == is only ever valid when both operands are of the same type.

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These differences in notation have real impact. Encapsulation through data structures leads to better comprehension that monolithic or purely functional languages (Woodfield et al. 1981, Bhattacharya & Neamtiu). Declarative programming paradigms (like the JavaScript React) have greater comprehensibility than imperative languages (Salvaneschi et al. 2014). In general, languages that are statically typed result in fewer defects (Ray et la. 2014), better comprehensibility because of the ability to construct better documentation (Endrikat et al. 2014), and result in easier debugging (Hanenberg et al. 2013). In fact, studies of more dynamic languages like JavaScript and Smalltalk (Callaú et al. 2013) show that the dynamic features of these languages aren't really used all that much anywhere. It appears then that the more you tell a compiler about what your code means, the more it helps the other developers know what it means too.

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Code editors, development environments, and program comprehension tools can also be helpful. Early evidence showed that simple features like syntax highlighting and careful typographic choices can improve the speed of program comprehension (Baecker 1988). I have also worked on several tools to support program comprehension, including the Whyline, which automates many of the more challenging aspects of navigating dependencies in code, and visualizes them (Ko & Myers 2009):

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The path from novice to expert in program comprehension is one that involves understanding programming language semantics exceedingly well and reading a lot of code, design patterns, and architectures. Anticipate that as you develop these skills, it will take you time to build robust understandings of what a program is doing, slowing down your writing, testing, and debugging.

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Next chapter: Verification
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Further reading

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R. Baecker. 1988. Enhancing program readability and comprehensibility with tools for program visualization. In Proceedings of the 10th international conference on Software engineering (ICSE '88). IEEE Computer Society Press, Los Alamitos, CA, USA, 356-366.

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Pamela Bhattacharya and Iulian Neamtiu. 2011. Assessing programming language impact on development and maintenance: a study on C and C++. In Proceedings of the 33rd International Conference on Software Engineering (ICSE '11). ACM, New York, NY, USA, 171-180.

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Binkley, D., Davis, M., Lawrie, D., Maletic, J. I., Morrell, C., & Sharif, B. (2013). The impact of identifier style on effort and comprehension. Empirical Software Engineering, 18(2), 219-276.

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Callaú, O., Robbes, R., Tanter, É., & Röthlisberger, D. (2013). How (and why) developers use the dynamic features of programming languages: the case of Smalltalk. Empirical Software Engineering, 18(6), 1156-1194. +

Barthélémy Dagenais, Harold Ossher, Rachel K. E. Bellamy, Martin P. Robillard, and Jacqueline P. de Vries. 2010. Moving into a new software project landscape. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1 (ICSE '10), Vol. 1. ACM, New York, NY, USA, 275-284.

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Stefan Endrikat, Stefan Hanenberg, Romain Robbes, and Andreas Stefik. 2014. How do API documentation and static typing affect API usability? In Proceedings of the 36th International Conference on Software Engineering (ICSE 2014). ACM, New York, NY, USA, 632-642.

Green, T. R. (1989). Cognitive dimensions of notations. People and computers V, 443-460.

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Fleming, S. D., Scaffidi, C., Piorkowski, D., Burnett, M., Bellamy, R., Lawrance, J., & Kwan, I. (2013). An information foraging theory perspective on tools for debugging, refactoring, and reuse tasks. ACM Transactions on Software Engineering and Methodology (TOSEM), 22(2), 14.

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Stefan Hanenberg, Sebastian Kleinschmager, Romain Robbes, Éric Tanter, Andreas Stefik. An empirical study on the impact of static typing on software maintainability. Empirical Software Engineering. 2013.

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Ko, A. J., & Myers, B. A. (2009, April). Finding causes of program output with the Java Whyline. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1569-1578).

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Thomas D. LaToza and Brad A. Myers. 2010. Developers ask reachability questions. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1 (ICSE '10), Vol. 1. ACM, New York, NY, USA, 185-194.

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Thomas D. LaToza, David Garlan, James D. Herbsleb, and Brad A. Myers. 2007. Program comprehension as fact finding. In Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering (ESEC-FSE '07). ACM, New York, NY, USA, 361-370.

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Walid Maalej, Rebecca Tiarks, Tobias Roehm, and Rainer Koschke. 2014. On the Comprehension of Program Comprehension. ACM Transactions on Software Engineering and Methodology. 23, 4, Article 31 (September 2014), 37 pages.

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A. von Mayrhauser and A. M. Vans. 1994. Comprehension processes during large scale maintenance. In Proceedings of the 16th international conference on Software engineering (ICSE '94). IEEE Computer Society Press, Los Alamitos, CA, USA, 39-48.

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Baishakhi Ray, Daryl Posnett, Vladimir Filkov, and Premkumar Devanbu. 2014. A large scale study of programming languages and code quality in GitHub. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2014). ACM, New York, NY, USA, 155-165.

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Tobias Roehm, Rebecca Tiarks, Rainer Koschke, and Walid Maalej. 2012. How do professional developers comprehend software? In Proceedings of the 34th International Conference on Software Engineering (ICSE '12). IEEE Press, Piscataway, NJ, USA, 255-265.

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Guido Salvaneschi, Sven Amann, Sebastian Proksch, and Mira Mezini. 2014. An empirical study on program comprehension with reactive programming. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2014). ACM, New York, NY, USA, 564-575.

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Jonathan Sillito, Gail C. Murphy, and Kris De Volder. 2006. Questions programmers ask during software evolution tasks. In Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering (SIGSOFT '06/FSE-14). ACM, New York, NY, USA, 23-34.

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S. N. Woodfield, H. E. Dunsmore, and V. Y. Shen. 1981. The effect of modularization and comments on program comprehension. In Proceedings of the 5th international conference on Software engineering (ICSE '81). IEEE Press, Piscataway, NJ, USA, 215-223.

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Andreas Stefik and Susanna Siebert. 2013. An Empirical Investigation into Programming Language Syntax. ACM Transactions on Computing Education 13, 4, Article 19 (November 2013), 40 pages.

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Yida Tao, Yingnong Dang, Tao Xie, Dongmei Zhang, and Sunghun Kim. 2012. How do software engineers understand code changes? An exploratory study in industry. In Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering (FSE '12). ACM, New York, NY, USA, , Article 51 , 11 pages.

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Mark Weiser. 1981. Program slicing. In Proceedings of the 5th international conference on Software engineering (ICSE '81). IEEE Press, Piscataway, NJ, USA, 439-449.

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+ +

Podcasts

+ + + +

Software Engineering Daily, Language Design with Brian Kernighan.

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+ + + + + + + diff --git a/debugging.html b/debugging.html new file mode 100644 index 0000000..0139823 --- /dev/null +++ b/debugging.html @@ -0,0 +1,112 @@ + + + + + + + + + + + + + + + + + + Debugging + + + +

Back to table of contents

+ + + Credit: public domain + +

Debugging

+
Andrew J. Ko
+ +

Despite all of your hard work at design, implementation, and verification, your software has failed. Somewhere in its implementation there's a line of code, or multiple lines of code, that, given a particular set of inputs, causes the program to fail. How do you find those defective lines of code?

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To start, you have to reproduce the failure. Failure reproduction is a matter of identifying inputs to the program (whether data it receives upon being executed, user inputs, network traffic, or any other form of input) that causes the failure to occur. If you found this failure while you were executing the program, then you're lucky: you should be able to repeat whatever you just did and identify the inputs or series of inputs that caused the problem, giving you a way of testing that the program no longer fails once you've fixed the defect. If someone else was the one executing the program (for example, a user, or someone on your team), you better hope that they reported clear steps for reproducing the problem. When bug reports lack clear reproduction steps, bugs often can't be fixed (Bettenburg et al. 2008).

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If you can reproduce the problem, the next challenge is to localize the defect, trying to identify the cause of the failure in code. There are many different strategies for localizing defects. At the highest level, one can think of this process as a hypothesis testing activity (Gilmore 1991):

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  1. Observe failure
  2. +
  3. Form hypothesis of cause of failure
  4. +
  5. Devise a way to test hypothesis, such as analyzing the code you believe caused it or executing the program with the reproduction steps and stopping at the line you believe is wrong.
  6. +
  7. If the hypothesis was supported (meaning the program failed for the reason you thought it did), stop. Otherwise, return to 1.
  8. +
+ +

The problems with the strategy above are numerous. First, what if you can't think of a possible cause? Second, what if your hypothesis is way off? You could spend hours generating hypotheses that are completely off base, effectively analyzing all of your code before finding the defect.

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Another strategy is working backwards (Ko & Myers):

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    +
  1. Observe failure
  2. +
  3. Identify the line of code that caused the failing output
  4. +
  5. Identify the lines of code that caused the line of code in step 2 and any data used on the line in step 2
  6. +
  7. Repeat three recursively, analyzing all lines of code for defects along the chain of causality
  8. +
+ +

The nice thing about this strategy is that you're guaranteed to find the defect if you can accurately identify the causes of each line of code contributing to the failure. It still requires you to analyze each line of code and potentially execute to it in order to inspect what might be wrong, but it requires potentially less work than guessing. My doctoral dissertation work investigated how to automate this strategy, allowing you to simply click on the fault output and then immediately see all upstream causes of it (Ko & Myers).

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Yet another strategy called delta debugging is to compare successful and failing executions of the program (Zeller 2002):

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    +
  1. Identify a successful set of inputs
  2. +
  3. Identify a failing set of inputs
  4. +
  5. Compare the differences in state from the successful and failing executions
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  7. Identify a change to input that minimizes the differences in states between the two executions
  8. +
  9. Variables and values that are different in these two executions contain the defect
  10. +
+ +

This is a powerful strategy, but only when you have successful inputs and when you can automate comparing runs and identifying changes to inputs.

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One of the simplest strategies is to work forward:

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    +
  1. Execute the program with the reproduction steps
  2. +
  3. Step forward one instruction at a time until the program deviates from intended behavior
  4. +
  5. This step that deviates or one of the previous steps caused the failure
  6. +
+ +

This strategy is easy to follow, but can take a long time because there are so many instructions that can execute.

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For particularly complex software, it can sometimes be necessary to debug with the help of teammates, helping to generate hypotheses, identify more effective search strategies, or rule out the influence of particular components in a bug (Aranda and Venolia 2009).

+ +

Ultimately, all of these strategies are essentially search algorithms, seeking the events that occurred while a program executed with a particular set of inputs that caused its output to be incorrect. Because programs execution millions and potentially billions of instructions, these strategies are necessary to reduce the scope of your search.

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Once you've found the defect, what do you do? It turns out that there are usually many ways to repair a defect. How professional developers fix defects depends a lot on the circumstances: if they're near a release, they may not even fix it if it's too risky; if there's no pressure, and the fix requires major changes, they may refactor or even redesign the program to prevent the failure (Murphy-Hill et al. 2013). This can be a delicate, risky process: in one study of open source operating systems bug fixes, 27% of the incorrect fixes were made by developers who had never read the source code files they changed, suggesting that key to correct fixes is a deep comprehension of exactly how the defective code is intended to behave (Yin et al. 2011).

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Further reading

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Jorge Aranda and Gina Venolia. 2009. The secret life of bugs: Going past the errors and omissions in software repositories. In Proceedings of the 31st International Conference on Software Engineering (ICSE '09). IEEE Computer Society, Washington, DC, USA, 298-308.

+

Nicolas Bettenburg, Sascha Just, Adrian Schröter, Cathrin Weiss, Rahul Premraj, and Thomas Zimmermann. 2008. What makes a good bug report? In Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering (SIGSOFT '08/FSE-16). ACM, New York, NY, USA, 308-318.

+

Gilmore, D. (1991). Models of debugging. Acta Psychologica, 78, 151-172.

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Andrew J. Ko and Brad A. Myers. 2008. Debugging reinvented: asking and answering why and why not questions about program behavior. In Proceedings of the 30th international conference on Software engineering (ICSE '08). ACM, New York, NY, USA, 301-310.

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Emerson Murphy-Hill, Thomas Zimmermann, Christian Bird, and Nachiappan Nagappan. 2013. The design of bug fixes. In Proceedings of the 2013 International Conference on Software Engineering (ICSE '13). IEEE Press, Piscataway, NJ, USA, 332-341.

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Zuoning Yin, Ding Yuan, Yuanyuan Zhou, Shankar Pasupathy, and Lakshmi Bairavasundaram. 2011. How do fixes become bugs? In Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering (ESEC/FSE '11). ACM, New York, NY, USA, 26-36.

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Andreas Zeller. 2002. Isolating cause-effect chains from computer programs. In Proceedings of the 10th ACM SIGSOFT symposium on Foundations of software engineering (SIGSOFT '02/FSE-10). ACM, New York, NY, USA, 1-10.

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+ +

Podcasts

+ + + +

Software Engineering Daily, Debugging Stories with Haseeb Qureshi

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Back to table of contents

+ + + Credit: public domain + +

Evolution

+
Andrew J. Ko
+ +

Programs change. You find bugs, you fix them. You discover a new requirement, you add a feature. A requirement changes because the world changes, you revise a feature. The simple fact about programs are that they're rarely stable, but rather a constantly changing, living documents that shift as much as the world around them shift.

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Nowhere is this constant evolution more apparent then in our daily encounters with software updates. The apps on our phones are constantly being updated to improve our experiences, while the web sites we visit potentially change every time we visit them, without us noticing. These different models have different notions of who controls changes to user experience: should software companies control when your experience changes or should you? And with systems with significant backend dependencies, is it even possible to give users control over when things change?

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To manage change, developers use all kinds of tools and practices.

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One of the most common ways of managing change is to refactor code. Refactoring helps developers modify the architecture of a program while keeping its behavior the same, enabling them to implement or modify functionality more easily. For example, one of the most common and simple refactorings is to rename a variable (renaming its definition and all of its uses). This doesn't change the architecture of a program at all, but does improve its readability. Other refactors can be more complex. For example, consider adding a new parameter to a function: all calls to that function need to pass that new parameter, which means you need to go through each call and decide on a value to send from that call site. Studies of refactoring in practice have found that refactorings can be big and small, that they don't always preserve the behavior of a program, and that developers perceive them as involving substantial costs and risks (Kim et al. 2012).

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Another fundamental way that developers manage change is version control systems. As you know, they help developers track changes to code, allowing them to revert, merge, fork, and clone projects in a way that is traceable and reliable. While today the most popular version control system is Git, there are actually many types. Some are centralized, representing one single ground truth of a project's code, usually stored on a server. Commits to centralized repositories become immediately available to everyone else on a project. Other version control systems are distributed, such as Git, allowing one copy of a repository on every local machine. Commits to these local copies don't automatically go to everyone else; rather, they are pushed to some central copy, from which others can pull updates. Research comparing centralized and distributed revision control systems mostly reveal tradeoffs rather than a clear winner. Distributed version control, for example, appears to lead to commits that are smaller and more scoped to single changes, since developers can manage their own history of commits to their local repository (Brindescu et al. 2014). Google uses one big centralized version control repository for all of its projects, however, because it offers one source of truth, simplified dependency management, large-scale refactoring, and flexible team boundaries (Potvin & Levenberg 2016).

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When code changes, you need to test it, which often means you need to build it, compiling source, data, and other resources into an executable format suitable for testing (and possibly release). Build systems can be as simple as nothing (e.g., loading an HTML file in a web browser interprets the HTML and displays it, requiring no special preparation) and as complex is hundreds and thousands of lines of build script code, compiling, linking, and managing files in a manner that prepares a system for testing, such as those used to build operating systems like Windows or Linux. To write these complex build procedures, developers use build automation tools like make, ant, gulp and dozens of others, each helping to automate builds. In large companies, there are whole teams that maintain build automation scripts to ensure that developers can always quickly build and test. In these teams, most of the challenges are social and not technical: teams need to clarify role ambiguity, knowledge sharing, communication, trust, and conflict in order to be productive, just like other software engineering teams (Phillips et al. 2014).

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Perhaps the most modern form of build practice is continuous integration. This is the idea of completely automating not only builds, but also the running of a collection of tests, every time a bundle of changes is pushed to a central version control repository. The claimed benefit of continuous integration is that every major change is quickly built, tested, and ready for deployment, shortening the time between a change and the discovery of failures. This only works if builds are fast. For example, some large projects like Windows can take a whole day to build, making continuous integration of the whole operating system infeasible. When builds and tests are fast, continuous integration can accelerate development, especially in projects with large numbers of contributors (Vasilescu et al. 2015) + +

One last problem with changes in software is managing the releases of software. Good release management should archive new versions of software, automatically post the version online, make the version accessible to users, keep a history of who accesses the new version, and provide clear release notes describing changes from the previous version (van der Hoek et al. 1997). By default, all of this is quite manual, but many of these steps can be automated, streamlining how teams release changes to the world. You've probably encountered these most in the form of software updates to applications and operating systems.

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Next chapter: Debugging
+ +

Further reading

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Caius Brindescu, Mihai Codoban, Sergii Shmarkatiuk, and Danny Dig. 2014. How do centralized and distributed version control systems impact software changes? In Proceedings of the 36th International Conference on Software Engineering (ICSE 2014). ACM, New York, NY, USA, 322-333.

+

Miryung Kim, Thomas Zimmermann, and Nachiappan Nagappan. 2012. A field study of refactoring challenges and benefits. In Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering (FSE '12).

+

Shaun Phillips, Thomas Zimmermann, and Christian Bird. 2014. Understanding and improving software build teams. In Proceedings of the 36th International Conference on Software Engineering (ICSE 2014). ACM, New York, NY, USA, 735-744.

+

Potvin, R., & Levenberg, J. (2016). Why Google stores billions of lines of code in a single repository. Communications of the ACM, 59(7), 78-87.

+

Bogdan Vasilescu, Yue Yu, Huaimin Wang, Premkumar Devanbu, and Vladimir Filkov. 2015. Quality and productivity outcomes relating to continuous integration in GitHub. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2015). ACM, New York, NY, USA, 805-816.

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André van der Hoek, Richard S. Hall, Dennis Heimbigner, and Alexander L. Wolf. 1997. Software release management. In Proceedings of the 6th European SOFTWARE ENGINEERING conference held jointly with the 5th ACM SIGSOFT international symposium on Foundations of software engineering (ESEC '97/FSE-5), Mehdi Jazayeri and Helmut Schauer (Eds.). Springer-Verlag New York, Inc., New York, NY, USA, 159-175.

+ +
+ +

Podcasts

+ + + +

Software Engineering Daily, Continuous Delivery with David Rice.

+ +
+ + + + + + + + diff --git a/history.html b/history.html new file mode 100644 index 0000000..bd1d60b --- /dev/null +++ b/history.html @@ -0,0 +1,82 @@ + + + + + + + + + + + + + + + + + + + The history of software engineering + + + +

Back to table of contents

+ + + + Credit: unknown + + +

A brief history of software engineering

+
Andrew J. Ko
+ +

Computers haven't been around for long. If you read one of the many histories of computing and information, such as James Gleick's The Information, or Jonathan Grudin's History of HCI, you'll learn that before digital computers, computers were people, calculating things manually. And that after digital computers, programming wasn't something that many people did. It was reserved for whoever had access to the mainframe and they wrote their programs on punchcards like the one above. Computing was in no way a ubiquitous, democratized activity—it was reserved for the few that could afford and maintain a room-sized machine.

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Because programming required such painstaking planning in machine code and computers were slow, most programs were not that complex. Their value was in calculating things faster than a person could do by hand, which meant thousands of calculations in a minute rather than one calculation in a minute. Computer programmers were not solving problems that had no solutions; they were translating existing solutions (for example, a quadratic formula) into the notation a computer understood. Their power wasn't in creating new realities or facilitating new tasks, it was accelerating old tasks.

+ +

The birth of software engineering, therefore, did not come until programmers started solving problems that didn't have existing solutions, or were new ideas entirely. Most of these were done in academic contexts to develop things like basic operating systems and methods of input and output. These were complex projects, but as research, they didn't need to scale; they just needed to work. It wasn't until the late 1960's, when the first truly large software projects were attempted commercially, and software had to actually work.

+ +

The IBM 360 operating system was one of the first big projects of this kind. Suddenly, there were multiple people working on multiple components, all which interacted. Different parts of the program needed to coordinate, which meant different people needed to coordinate, and the term software engineering was born. Programmers and academics from around the world, especially those that were working on big projects, began to start conferences so they could meet and discuss their challenges. In the first software engineering conference in 1968, attendees speculated about why projects were shipping late, why they were over budget, and what to do about it.

+ +

In these early days of software engineering, programmers, managers, and researchers discovered many problems that had no clear solutions:

+ + + +

These questions are at the foundation of the field of software engineering and are the core content of this course. Some of them have pretty good answers. For example, the research community rapidly converged toward the concept of a version control systems, software testing, and a wide array of high-level programming languages such as Fortran (Metcalf 2002), LISP (McCarthy 1978), C++ (Stroustrup 1996), and Smalltalk (Kay 1996), all of which were precursors to today's modern languages such as Java, Python, and JavaScript.

+ +

Other questions, particularly those concerning the human aspects of software engineering, were hopelessly difficult to understand and improve. One of the seminal books on these issues was Fred P. Brooks, Jr.'s The Mythical Man Month. In it, he presented hundreds of claims about software engineering. For example, he hypothesized that adding more programmers to a project would actually make productivity worse at some level, not better, because of the added burden of knowledge sharing. He also claimed that the first implementation of a solution is usually terrible and should be treated like a prototype: used to learn and then discarded. These and other claims have been the foundation of decades of years of research, all in search of some deeper answer to the questions above.

+ +

If we step beyond software engineering and think more broadly about the role that software is playing in society today, there are also other, newer questions that we've only begun to answer. If every part of society now runs on code, what responsibility do software engineers have to ensure that code is right? What responsibility do software engineers have to avoid algorithmic bias? If our cars are to soon drive us around, who's responsible for the first death: the car, the driver, or the software engineers who built it? These ethical questions are in some ways the future of software engineering, likely to shape it's regulatory context, its processes, and it's responsibilities.

+ +

There are also economic roles that software plays in society that it didn't before. Around the world, it's a major source of job growth, but also a major source of automation, eliminating jobs that people used to do. These larger forces that software is playing on the world demand that software engineers have a stronger understanding of the roles that software plays in society, as the decisions that engineers make can have profoundly impactful unintended consequences.

+ +

We're nowhere close to having deep answers about these questions, neither the old ones or the new ones. We know a lot about programming languages and a lot about testing. These are areas amenable to automation and so computer science has rapidly improved and accelerated these parts of software engineering. The rest of it, as we shall see in this, has not made much progress. In this class, we'll discuss what we know and the much larger space of what we don't.

+ +
Next chapter: Organizations
+ +

Further reading

+ +

Brooks Jr, F. P. (1995). The Mythical Man-Month (anniversary ed.). Chicago

+

Gleick, James (2011). The Information: A History, A Theory, A Flood. Pantheon Books.

+

Grudin, Jonathan (2017). From Tool to Partner: The Evolution of Human-Computer Interaction.

+

Kay, A. C. (1996, January). The early history of Smalltalk. In History of programming languages---II (pp. 511-598). ACM.

+

Ko, A. J. (2016). Interview with Andrew Ko on Software Engineering Daily about Software Engineering Research and Practice.

+

McCarthy, J. (1978, June). History of LISP. In History of programming languages I (pp. 173-185). ACM.

+

Metcalf, M. (2002, December). History of Fortran. In ACM SIGPLAN Fortran Forum (Vol. 21, No. 3, pp. 19-20). ACM.

+

Stroustrup, B. (1996, January). A history of C++: 1979--1991. In History of programming languages---II (pp. 699-769). ACM.

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