02:00 - 02:20

• Mining Software Repositories for Social Norms

  Social norms facilitate coordination and cooperation among individuals, thus enable smoother functioning of social groups such as the highly distributed and diverse open source software development (OSSD) communities. In these communities, norms are mostly implicit and hidden in huge records of human-interaction information such as emails, discussions threads, bug reports, commit messages and even source code. This paper aims to introduce a new line of research on extracting social norms from the rich data available in software repositories. Initial results include a study of coding convention violations in JEdit, Argo UML and Glassfish projects. It also presents a new life-cycle model for norms in OSSD communities and demonstrates how a number of norms extracted from the Python development community follow this life-cycle model.

Hoa Khanh Dam; Bastin Tony Roy Savarimuthu; Daniel Avery; Aditya Ghose

University of Wollongong, Australia; University of Otago, New Zealand

02:20 - 02:40

• Commit Bubbles

  Developers who use version control are expected to produce systematic commit histories that show well-defined steps with logical forward progress. Existing version control tools assume that developers also write code systematically. Unfortunately, the process by which developers write source code is often evolutionary, or as-needed, rather than systematic. Our contribution is a fragment-oriented concept called Commit Bubbles that will allow developers to construct systematic commit histories that adhere to version control best practices with less cognitive effort, and in a way that integrates with their as-needed coding workflows.

Titus Barik; Kevin Lubick; Emerson Murphy-Hill

North Carolina State University, USA

02:40 - 02:50

• Rapid Multi-Purpose, Multi-Commit Code Analysis

  Existing code- and software evolution studies typically operate on the scale of a few revisions of a small number of projects, mostly because existing tools are unsuited for performing large-scale studies. We present a novel approach, which can be used to analyze an arbitrary number of revisions of a software project simultaneously and which can be adapted for the analysis of mixed-language projects. It lays the foundation for building high-performance code analyzers for a variety of scenarios. We show that for one particular scenario, namely code metric computation, our prototype outperforms existing tools by multiple orders of magnitude when analyzing thousands of revisions.

Carol Alexandru; Harald Gall

University of Zurich, Switzerland

02:50 - 03:00

• Leveraging Informal Documentation to Summarize Classes and Methods in Context

  Critical information related to a software developer's current task is trapped in technical developer discussions, bug reports, code reviews, and other software artefacts. Much of this information pertains to the proper use of code elements (e.g., Methods and classes) that capture vital problem domain knowledge. To understand the purpose of these code elements, software developers must either access documentation and online posts and understand the source code or peruse a substantial amount of text. In this paper, we use the context that surrounds code elements in Stack Overflow posts to summarize the use and purpose of code elements. To provide focus to our investigation, we consider the generation of summaries for library identifiers discussed in Stack Overflow. Our automatic summarization approach was evaluated on a sample of 100 randomly-selected library identifiers with respect to a benchmark of summaries provided by two annotators. The results show that the approach attains an R-precision of 54%, which is appropriate given the diverse ways in which code elements can be used.

Latifa Guerrouj; David Bourque; Peter C. Rigby

Concordia University, Canada