Enriching Reverse Engineering with Semantic Clustering

Kuhn, Adrian; Ducasse, Sté́phane; Gîrba, Tudor

doi:10.1109/wcre.2005.16

Cited by 77 publications

(54 citation statements)

References 13 publications

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“…Concept location concerns how high-level concepts, such as bugs, relate to low level entities such as source code. Semantic Clustering has also been used for similar purposes [7,8] as it is similar to LSI.…”

Section: Lda Lsi and Semantic Clusteringmentioning

confidence: 99%

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What's hot and what's not: Windowed developer topic analysis

Hindle

Godfrey

Holt

2009

2009 IEEE International Conference on Software Maintenance

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As development on a software project progresses, developers shift their focus between different topics and tasks many times. Managers and newcomer developers often seek ways of understanding what tasks have recently been worked on and how much effort has gone into each; for example, a manager might wonder what unexpected tasks occupied their team's attention during a period when they were supposed to have been implementing new features. Tools such as Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) can be used to extract a set of independent topics from a corpus of commit-log comments. Previous work in the area has created a single set of topics by analyzing comments from the entire lifetime of the project. In this paper, we propose windowing the topic analysis to give a more nuanced view of the system's evolution. By using a defined time-window of, for example, one month, we can track which topics come and go over time, and which ones recur. We propose visualizations of this model that allows us to explore the evolving stream of topics of development occurring over time. We demonstrate that windowed topic analysis offers advantages over topic analysis applied to a project's lifetime because many topics are quite local.

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Section: Lda Lsi and Semantic Clusteringmentioning

confidence: 99%

“…In terms of clustering and finding topic distributions, Latent Dirichlet Allocation (LDA) [2] competes with Latent Semantic Indexing (LSI) [11,12], probabilistic Latent Semantic Indexing (pLSI) [2] and semantic clustering [7,8]. These tools are used for document modelling, document clustering and collaborative filtering.…”

Section: Introductionmentioning

confidence: 99%

What's hot and what's not: Windowed developer topic analysis

Hindle

Godfrey

Holt

2009

2009 IEEE International Conference on Software Maintenance

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“…Correlating architectural elements (those of the generated views of the framework) with architectural concepts (those of domain's viewpoints) is useful to enrich a system understanding, i.e. what the code is about [10] and how this knowledge disseminates and is found in the implementation view. This paper proposes a generic and recursive approach for considering some knowledge (as viewpoint) into reconstruction process of existing system.…”

Section: Discussionmentioning

confidence: 99%

Multiple viewpoints architecture extraction

Razavizadeh

Verjus

Cimpan

et al. 2009

2009 Joint Working IEEE/IFIP Conference on Software Architecture &Amp; European Conference on Software Architecture

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Abstract-A software system's architecture, its elements and the way they interact, constitute valuable assets for comprehending the system. Many approaches have been developed to help comprehending software systems in different manners. Most of them focus on structural aspects. We believe offering multiple views of the same system, using domain knowledge helps understanding a software system as whole.To correlate domain information and existing software systems, different viewpoints are considered and modelled. Viewpoints guide the extraction of architectural views, the later representing different system facets. We propose a recursive framework, an approach that expresses domain knowledge as viewpoints to guide the extraction process. It provides multiple architectural views according to multiple given viewpoints.

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“…In particular, it has been used to recover traceability links between different software artifacts, using Vector Space Models and Probabilistic Ranking [1], or Latent Semantic Indexing (LSI) [76]. Also, textual analysis using Information Retrieval techniques has been used to perform a software quality assessment based on the similarity between identifiers and comments [73], to measure the conceptual cohesion of classes [77], or to perform semantic clustering [69].…”

Section: Program Analysis and Its Applicationsmentioning

confidence: 99%