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Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval

by Marcus, A. , Antoniol, G. , Gueheneuc, Y.-G. , Poshyvanyk, D. , Rajlich, V.

in Case studies / Computer bugs / Computer Society / concept location / Decision making / Digital Object Identifier / dynamic and static analyses / feature identification / Impact analysis / Indexing / Information analysis / Information retrieval / Latent Semantic Indexing / Open source software / Performance analysis / Position (location) / Probabilistic methods / Probability theory / Program understanding / Programming profession / Ranking / scenario-based probabilistic ranking / Semantics / Software engineering / Source code / Studies / Uncertainty

2007

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Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval

by Marcus, A. , Antoniol, G. , Gueheneuc, Y.-G. , Poshyvanyk, D. , Rajlich, V.

2007

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Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval

by Marcus, A. , Antoniol, G. , Gueheneuc, Y.-G. , Poshyvanyk, D. , Rajlich, V.

2007

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Journal Article

Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval

Marcus, A.,

Antoniol, G.,

Gueheneuc, Y.-G.,

Poshyvanyk, D.,

Rajlich, V.

2007

Overview

This paper recasts the problem of feature location in source code as a decision-making problem in the presence of uncertainty. The solution to the problem is formulated as a combination of the opinions of different experts. The experts in this work are two existing techniques for feature location: a scenario-based probabilistic ranking of events and an information-retrieval-based technique that uses latent semantic indexing. The combination of these two experts is empirically evaluated through several case studies, which use the source code of the Mozilla Web browser and the Eclipse integrated development environment. The results show that the combination of experts significantly improves the effectiveness of feature location as compared to each of the experts used independently

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Publisher

IEEE,IEEE Computer Society

Subject

/ Digital Object Identifier

/ dynamic and static analyses