Learning natural coding conventions

Abstract

Every programmer has a characteristic style, ranging from preferences about identifier naming to preferences about object relationships and design patterns. Coding conventions define a consistent syntactic style, fostering readability and hence maintainability. When collaborating, programmers strive to obey a project's coding conventions. However, one third of reviews of changes contain feedback about coding conventions, indicating that programmers do not always follow them and that project members care deeply about adherence. Unfortunately, programmers are often unaware of coding conventions because inferring them requires a global view, one that aggregates the many local decisions programmers make and identifies emergent consensus on style. We present NATURAL-IZE, a framework that learns the style of a codebase, and suggests revisions to improve stylistic consistency. NATURALIZE builds on recent work in applying statistical natural language processing to source code. We apply NATURALIZE to suggest natural identifier names and formatting conventions. We present four tools focused on ensuring natural code during development and release management, including code review. NATURALIZE achieves 94% accuracy in its top suggestions for identifier names and can even transfer knowledge about conventions across projects, leveraging a corpus of 10,968 open source projects. We used NATURALIZE to generate 18 patches for 5 open source projects: 14 were accepted.

Using Naming Patterns for Identifying Architectural Technical Debt

“…MATCH(t:FrequentTerm:PPackage), (p:Package {fullName:t.packageFullName}) RETURN t,p Code conventions can often be expressed as common practices which follows certain consensus before than as imposed rules [19]. The proposed analysis enables identifying a consensus of terms in following the naming patterns.…”

“…The relevance of class names lies in determining the code legibility, portability, maintainability, and accessibility to new team members, and relating the source code to the problem domain [19]. Also, industry experts highlight the importance of identifier names in software [12,28,31].…”

End-to-End Deep Learning of Optimization Heuristics

“…These include mining coding conventions [14] and idioms [13], API example code [48] and pseudo-code generation [49], and benchmark generation [7]. Our work is the first attempt to extend the already challenging task of modeling distributions over source code to learning distributions over source code with respect to code optimizations.…”

“…Our insight was that such costly approaches are not necessary any more. Deep learning techniques have shown astounding successes in identifying complex patterns and relationships in images [10,11], audio [12], and even computer code [7,13,14]. We hypothesized that deep neural networks should be able to automatically extract features from source code.…”

Learning from Human Repairs Through the Exploitation of Software Repositories

“…According to Allamanis [21], coding conventions define a coherent syntactic style, promoting readability and thus maintainability. Coding conventions (naming, formatting decisions) determine the clarity of the source code, increasing code portability, accessibility to new coders, reliability and maintainability.…”