Incorporating Wikis into software repository mining

2008 Frontiers of Software Maintenance, 2008

Source control repositories, bug repositories, archived communications, deployment logs, and code repositories are examples of software repositories that are commonly available for most software projects. The Mining Software Repositories (MSR) field analyzes and cross-links the rich data available in these repositories to uncover interesting and actionable information about software systems. By transforming these repositories from static record-keeping ones into active repositories, we can guide decision processes in modern software projects. For example, data in source control repositories, traditionally used to archive code, could be linked with data in bug repositories to help practitioners propagate complex changes and to warn them about risky code based on prior changes and bugs. In this paper, we present a brief history of the MSR field and discuss several recent achievements and results of using MSR techniques to support software research and practice. We then discuss the various opportunities and challenges that lie in the road ahead for this important and emerging field.

Progress in Artificial Intelligence, 2018

A software process is a set of related activities that culminates in the production of a software package: specification, design, implementation, testing, evolution into new versions, and maintenance. There are also other supporting activities such as configuration and change management, quality assurance, project management, evaluation of user experience, etc. Software repositories are infrastructures to support all these activities. They can be composed with several systems that include code change management, bug tracking, code review, build system, release binaries, wikis, forums, etc. This position paper on mining software repositories presents a review and a discussion of research in this field over the past decade. We also identify applied machine learning strategies, current working topics, and future challenges for the improvement of company decision-making systems. Machine learning is defined as the process of discovering patterns in data. It can be applied to software repositories, since every change is recorded as data. Companies can then use these patterns as the basis for their decision-making systems and for knowledge discovery.

Empirical Software Engineering, 2009

The Mining Software Repositories (MSR) field analyzes and cross-links the rich data available in software repositories to uncover interesting and actionable information about software systems and projects. Examples of software repositories are:

Acm Sigsoft Software Engineering Notes, 2005

A one-day workshop was held on the topic of mining software repositories at ICSE 2004 in Edinburgh, Scotland. The workshop brought together researchers and practitioners in order to consider methods that use data stored in software repositories (such as source control systems, defect tracking systems, and archived project communications) to further understanding of software development practices. We divided submissions into six sessions, each devoted to a particular topic: 1) Infrastructure and Extraction, 2) Integration and Presentation, 3) System Understanding and Change Patterns, 4) Defect Analysis, 5) Process and Community Analysis, and 6) Software Reuse. To maximize interaction and discussion, we limited each session to a survey of the topic area, followed by the presentation of one or two papers, then an open discussion. We also allocated a demo hour to give interested parties the opportunity to learn more about other accepted papers.

2009

Most software development today is a team activity. Project team members collaboratively work on the tasks necessary to accomplish the various project milestones. The work is usually asynchronous, not orchestrated by any explicit workflow, sometimes geographically distributed, and involves the use of a variety of tools, which do not always interoperate. Version-control repositories are essential in supporting this collaboration but cannot satisfactorily address the problem of traceability of interdependencies among the artifacts produced by the individual tools. In the WikiDev 2.0 collaboration tool, we propose to address these problems by adopting a wiki as the central platform in which to integrate information about the various artifacts of interest, to cluster this information in clusters of relevant artifacts, and to present views on this information that cut across the individual tool boundaries. In this paper, we discuss the central clustering algorithm in WikiDev 2.0 and we evaluate its effectiveness with a case study.