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@ -20,7 +20,7 @@ The first conference, the IBM 360 project, and Hamilton's experiences on the Apo
* What kinds of tools and languages can accelerate a programmers work and help them prevent mistakes?
* How can projects not lose sight of the immense complexity of human needs, values, ethics, and policy that interact with engineering decisions?
As it became clear that software was not an incremental change in technology, but a profoundly disruptive one, countless communities began to explore these questions in research and practice. Black American entreprenuers began to explore how to use software to connect and build community well before the internet was ubiquitous, creating some of the first web-scale online communities and forging careers at IBM, ultimately to be suppressed by racism in the workplace and society<mcilwain19>. White entreprenuers in Silicon Valley began to explore ways to bring computing to the masses, bolstered by the immense capital investments of venture capitalists, who saw opportunities for profit through disruption<kenney00>. And academia, which had helped demonstrate the feasibility of computing and established its foundations, began to invent the foundational tools of software engineering including, version control systems, software testing, and a wide array of high-level programming languages such as Fortran<metcalf02>, LISP<mccarthy78>, C++<stroustrup96> and Smalltalk<kay96>, all of which inspired the design of today's most popular languages, including Java, Python, and JavaScript. And throughout, despite the central role of women in programming the first digital computers, managing the first major software engineering projects, and imagining how software could change the world, women were systematically excluded from all of these efforts, their histories forgotten, erased, and overshadowed by pervasive sexism in commerce and government<abbate12>.
As it became clear that software was not an incremental change in technology, but a profoundly disruptive one, countless communities began to explore these questions in research and practice. Black American entrepreneurs began to explore how to use software to connect and build community well before the internet was ubiquitous, creating some of the first web-scale online communities and forging careers at IBM, ultimately to be suppressed by racism in the workplace and society<mcilwain19>. White entrepreneurs in Silicon Valley began to explore ways to bring computing to the masses, bolstered by the immense capital investments of venture capitalists, who saw opportunities for profit through disruption<kenney00>. And academia, which had helped demonstrate the feasibility of computing and established its foundations, began to invent the foundational tools of software engineering including, version control systems, software testing, and a wide array of high-level programming languages such as Fortran<metcalf02>, LISP<mccarthy78>, C++<stroustrup96> and Smalltalk<kay96>, all of which inspired the design of today's most popular languages, including Java, Python, and JavaScript. And throughout, despite the central role of women in programming the first digital computers, managing the first major software engineering projects, and imagining how software could change the world, women were systematically excluded from all of these efforts, their histories forgotten, erased, and overshadowed by pervasive sexism in commerce and government<abbate12>.
While technical progress has been swift, progress on the _human_ aspects of software engineering, have been more difficult to understand and improve. One of the seminal books on these issues was Fred P. Brooks, Jr.'s _The Mythical Man Month_<brooks95>. 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 knowledge sharing would be an immense but necessary burden. He also claimed that the _first_ implementation of a solution is usually terrible and should be treated like a prototype: used for learning 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. And only recently have scholars begun to reveal how software and software engineering tends to encode, amplify, and reinforce existing structures and norms of discrimination by encoding it into data, algorithms, and software architectures<benjamin19>. These histories show that, just like any other human activity, there are strong cultural forces that shape how people engineer software together, what they engineer, and what affect that has on society.