Modeling the Evolution of Development Topics Using Dynamic Topic Models

• Main Authors: HU, Jianjun, SUN, Xiaobing, David LO, LI, Bin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3075
• Institution: Singapore Management University

As the development of a software project progresses, its complexity grows accordingly, making it difficult to understand and maintain. During software maintenance and evolution, software developers and stakeholders constantly shift their focus between different tasks and topics. They need to investigate into software repositories (e.g., revision control systems) to know what tasks have recently been worked on and how much effort has been devoted to them. For example, if an important new feature request is received, an amount of work that developers perform on ought to be relevant to the addition of the incoming feature. If this does not happen, project managers might wonder what kind of work developers are currently working on. Several topic analysis tools based on Latent Dirichlet Allocation (LDA) have been proposed to analyze information stored in software repositories to model software evolution, thus helping software stakeholders to be aware of the focus of development efforts at various time during software evolution. Previous LDA-based topic analysis tools can capture either changes on the strengths of various development topics over time (i.e., strength evolution) or changes in the content of existing topics over time (i.e., content evolution). Unfortunately, none of the existing techniques can capture both strength and content evolution. In this paper, we use Dynamic Topic Models (DTM) to analyze commit messages within a project's lifetime to capture both strength and content evolution simultaneously. We evaluate our approach by conducting a case study on commit messages of two well-known open source software systems, jEdit and PostgreSQL. The results show that our approach could capture not only how the strengths of various development topics change over time, but also how the content of each topic (i.e., words that form the topic) changes over time. Compared with existing topic analysis approaches, our approach can provide a more complete and valuable vi- w of software evolution to help developers better understand the evolution of their projects.