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Expectations, outcomes, and challenges of modern code review
TLDR
This study reveals that while finding defects remains the main motivation for review, reviews are less about defects than expected and instead provide additional benefits such as knowledge transfer, increased team awareness, and creation of alternative solutions to problems.
View on ACM
Software Engineering for Machine Learning: A Case Study
TLDR
A study conducted on observing software teams at Microsoft as they develop AI-based applications finds that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace.
View on IEEE
Mining email social networks
TLDR
This paper begins with a discussion of the infrastructure (including a novel use of Scientific Workflow software) and then discusses the approach to mining the email archives, and presents some preliminary results from the data analysis.
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Suggesting accurate method and class names
TLDR
A neural probabilistic language model for source code that is specifically designed for the method naming problem is introduced, and a variant of the model is introduced that is, to the knowledge, the first that can propose neologisms, names that have not appeared in the training corpus.
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Don't touch my code!: examining the effects of ownership on software quality
TLDR
It is found that in all cases, measures of ownership such as the number of low-expertise developers, and the proportion of ownership for the top owner have a relationship with both pre-release faults and post-release failures.
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Convergent contemporary software peer review practices
TLDR
A measure of the degree to which knowledge is shared during review shows that conducting peer review increases the number of distinct files a developer knows about by 66% to 150% depending on the project.
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Characteristics of Useful Code Reviews: An Empirical Study at Microsoft
TLDR
The proportion of useful comments made by a reviewer increases dramatically in the first year that he or she is at Microsoft but tends to plateau afterwards, and it is found that the more files that are in a change, the lower the proportion of comments in the code review that will be of value to the author of the change.
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What developers want and need from program analysis: An empirical study
TLDR
A multi-method investigation at Microsoft is mounted to understand what makes a program analyzer most attractive to developers, and sheds light on what functionality developers want from analyzers, including the types of code issues that developers care about.
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Deep learning type inference
TLDR
DeepTyper is proposed, a deep learning model that understands which types naturally occur in certain contexts and relations and can provide type suggestions, which can often be verified by the type checker, even if it could not infer the type initially.
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Fair and balanced?: bias in bug-fix datasets
TLDR
The lesson is drawn that bias is a critical problem that threatens both the effectiveness of processes that rely on biased datasets to build prediction models and the generalizability of hypotheses tested on biased data.
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