MUDABlue: An automatic categorization system for Open Source repositories

Kawaguchi, Shin‐ichi; Garg, Pankaj; Matsushita, Masayuki; Inoue, Katsuro

doi:10.1016/j.jss.2005.06.044

Cited by 104 publications

(75 citation statements)

References 18 publications

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“…LSI is equally popular in software engineering research, in areas such as program comprehension , automatic feature location (Marcus et al 2004;, software clustering and categorization (Maletic and Marcus 2000;Kuhn et al 2007;Kawaguchi et al 2006), clone detection , recommender systems (McCarey et al 2006;Ye and Fischer 2002) etc. Kuhn et al report that LSI performs bit worse for analyzing software because of the scarcity of words in source code ).…”

Section: Topic Modelingmentioning

confidence: 99%

Analyzing and mining a code search engine usage log

Bajracharya

Lopes

2010

Empir Software Eng

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This paper presents an analysis of a year long usage log of Koders, the first commercially available Internet-Scale code search engine (http://www.koders.com). The usage log comprises about ten million activities from more than three million users. Analysis of the usage data shows that despite of attracting a large number of visitors, Koders has a very sparse usage and that it lacks regular usage from many of its users. When compared to Web search, search behavior in Koders showed many similar patterns. A topic modeling analysis of the usage data shows what topics users of Koders are looking for. Observations on the prevalence of these topics among the users, and observations on how search and download activities vary across topics, lead to the conclusion that users who find code search engines usable are those who already know to a high level of specificity what to look for. This paper also presents a general categorization of these topics that provides insights on the different ways code search engine users express their queries. It identifies various forms of queries in Koders's log and the kinds of results addressed by the queries. It also provides several suggestions for improvements in code search engines based on the analysis of usage, topics, and query forms. The work presented in this paper is the first of its kind that reveals several insights on the usage of an Internet-Scale code search engine.

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Section: Topic Modelingmentioning

confidence: 99%

Analyzing and mining a code search engine usage log

Bajracharya

Lopes

2010

Empir Software Eng

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“…Comments might be outdated or even redundant to the source code [22]. As an example, in the following line of code, comments add no value to the code concept:…”

Section: Information Extractionmentioning

confidence: 99%

Source code indexing for automated tracing

Mahmoud

Niu

2011

Proceedings of the 6th International Workshop on Traceability in Emerging Forms of Software Engineering

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Requirements-to-source-code traceability employs information retrieval (IR) methods to automatically link requirements to the source code that implements them. A crucial step in this process is indexing, where partial and important information from the software artifacts is converted into a representation that is compatible with the underlying IR model. Source code demands special attention in the indexing process. In this paper, we investigate source code indexing for supporting automatic traceability. We introduce a feature diagram that captures the key components and their relationships in the domain of source code indexing. We then present an experiment to examine the features of the diagram and their dependencies. Results show that utilizing comments has a significant effect on traceability link generation, and stemming is required when comments are considered.

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“…In the first, we compare LACT head-to-head with MUDABlue [6] on a set of 41 open-source software systems written in C. In the second case study, we apply LACT to a set of 43 open-source software systems written in different programming languages and compare the results to the manual categorizations provided on SourceForge. The results of these studies are discussed below.…”

Section: Case Studiesmentioning

confidence: 99%

“…We evaluated LACT on the same 41 software systems used to evaluate MUDABlue in [6]. We explored the categorization results in terms of the number of topics and the distribution thresholds.…”

Section: Comparison With Mudabluementioning

confidence: 99%