Labeled topic detection of open source software from mining mass textual project profiles

Abstract

Nowadays open source software has become an indispensable basis for both individual and industrial software engineering. Various kinds of labeling mechanisms like categories, keywords and tags are used in open source communities to annotate projects and facilitate the discovery of certain software. However, as large amounts of software are attached with no/few labels or the existing labels are from different ontology space, it is still hard to retrieve potentially topic-relevant software. This paper highlights the valuable semantic information of project descriptions and labels, proposes labeled software topic detection (LSTD), a hybrid approach combining topic models and ranking mechanisms to detect and enrich the topics of software by mining the large amount of textual software profiles, which can be employed to do software categorization and tag recommendation. L-STD makes use of labeled LDA to capture the semantic correlations between labels and descriptions and then construct the label-based topic-word matrix. Based on the generated matrix and the generality of labels, LSTD designs a simple yet efficient algorithm to detect the latent topics of software that expressed as relevant and popular labels. Comprehensive evaluations are conducted on the large-scale datasets of representative open source communities and the results validate the effectiveness of LSTD.

A study of library migrations in Java

“…More recently, Wang et al . proposed an approach to assign tags to software projects using mining of existing projects tags and descriptions . These approaches can be used in our context to create groups of equivalent libraries but without any guarantee on the fact that a library of a group can be replaced by any other equivalent library of the group.…”

Improving topic model source code summarization

“…Topic models are widely-used in software engineering [16,19]. Unfortunately, the main weakness to topic modeling approaches is that there is no agreed-upon technique for labeling the topics so that those topics can be understood in context [28,16]. In other words, currently a programmer can read topics to determine what features are implemented in software, but it is difficult for the programmer to determine how those features interact, or which features are the most important.…”

Analysis and Detection of Information Types of Open Source Software Issue Discussions

“…Issue metadata, such as tags and labels, are designed to partially address the problem of information overload by allowing OSS stakeholders to quickly grasp the most important information such as the origin and the status of the issue. In practice, however, the metadata are often outdated, incomplete or even misleading [27]. Even when managed diligently, it usually only represents the interests of the project management team or the core developers and thus is inadequate in fulfilling the needs of the diverse OSS project participants.…”