Programmers' build errors: a case study (at google)

By Hyunmin Seo, Caitlin Sadowski, Sebastian G. Elbaum, Edward Aftandilian, Robert W. Bowdidge. 2014

189 citations recorded by the source corpus.

Building is an integral part of the software development process. However, little is known about the compiler errors that occur in this process. In this paper, we present an empirical study of 26.6 million builds produced during a period of nine months by thousands of developers. We describe the workflow through which those builds are generated, and we analyze failure frequency, compiler error types, and resolution efforts to fix those compiler errors. The results provide insights on how a large organization build process works, and pinpoints errors for which further developer support would be most effective.

Subfields: Computer Networks and Communications, Information Systems, Software, Program Analysis & Verification

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Citations in the corpus

Break-It-Fix-It: Unsupervised Learning for Program Repair (2021, background): “Development of an automatic code fixer is thus a key to enhancing programming productivity (Seo et al., 2014) and is an active area of research (Mesbah et al., 2019; Ding et al., 2020; Dinella et al., 2020).”
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback (2020, background): “These errors are commonly observed in both beginner programmers (Parihar et al., 2017) and professional developers (Seo et al., 2014), as well as in the predicted code of program synthesis (Kulal et al., 2019).”
Grounded Theory in Software Engineering Research: A Critical Review and Guidelines (2016, background): “…experiments where the dependent variable is ‘measured’ by combining the subjective ratings of expert judges [55], case studies with upfront hypotheses [63], interview studies where text is analyzed quantitatively [61] and mixed-method inquiries that combine questionnaires with case studies [60].”
DeepFix: Fixing Common C Language Errors by Deep Learning (2017, background): “Not only novice students but experienced developers also make such errors as found in a study of build errors at Google (Seo et al. 2014).”
SampleFix: Learning to Correct Programs by Sampling Diverse Fixes (2019, background): “Novice programmers and even experienced developers often struggled with these type of errors (Seo et al., 2014), which is usually due to lack of attention to the details of programs and/or programmer’s inexperience (Gupta et al.”