An exploratory study on assessing feature location techniques

Revelle, Meghan; Poshyvanyk, Denys

doi:10.1109/icpc.2009.5090045

Cited by 41 publications

(36 citation statements)

References 23 publications

(50 reference statements)

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“…IR-based methods have been applied to support practical tasks. For instance, IR methods have been successfully used to support feature location (Liu et al 2007;Eaddy et al 2008;Revelle and Poshyvanyk 2009;Revelle et al 2010;Dit et al 2012a;Dit et al 2012b;, traceability link recovery (Antoniol et al 2002;Hayes et al 2006;De Lucia et al 2007;Cleland-Huang et al 2010;Oliveto et al 2010;Gethers et al 2011), and impact analysis (Antoniol et al 2000;Canfora and Cerulo 2005;Poshyvanyk et al 2009;Gethers and Poshyvanyk 2010;Gethers et al 2012). We do not discuss other applications of IR-based techniques in the context of software maintenance due to space limitations; however, interested readers are referred to (Binkley and Lawrie 2010a;Binkley and Lawrie 2010b) for such an overview.…”

Section: Conceptual Information In Softwarementioning

confidence: 99%

Integrating conceptual and logical couplings for change impact analysis in software

2012

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The paper presents an approach that combines conceptual and evolutionary techniques to support change impact analysis in source code. Conceptual couplings capture the extent to which domain concepts and software artifacts are related to each other. This information is derived using Information Retrieval based analysis of textual software artifacts that are found in a single version of software (e.g., comments and identifiers in a single snapshot of source code). Evolutionary couplings capture the extent to which software artifacts were co-changed. This information is derived from analyzing patterns, relationships, and relevant information of source code changes mined from multiple versions in software repositories. The premise is that such combined methods provide improvements to the accuracy of impact sets compared to the two individual approaches.A rigorous empirical assessment on the changes of the open source systems Apache httpd, ArgoUML, iBatis, KOffice, and jEdit is also reported. The impact sets are evaluated at the file and method levels of granularity for all the software systems considered in the empirical evaluation.The results show that a combination of conceptual and evolutionary techniques, across several cutoff points and periods of history, provides statistically significant improvements in accuracy over either of the two techniques used independently. Improvements in F-measure values of up to 14%(from 3% to 17%) over the conceptual technique in ArgoUML at the method granularity, and up to 21% over the evolutionary technique in iBatis (from 9% to 30%) at the file granularity were reported.

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Section: Conceptual Information In Softwarementioning

confidence: 99%

Integrating conceptual and logical couplings for change impact analysis in software

2012

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“…The effectiveness measure is an accepted metric to evaluate feature location techniques. It is 6 used here instead of precision and recall to be consistent with previous approaches [22,25] and because feature location techniques have been shown to be better at finding one relevant method for a feature as opposed to many [26].…”

Section: A Systems and Benchmarksmentioning

confidence: 99%

Using Data Fusion and Web Mining to Support Feature Location in Software

Revelle

Dit

Poshyvanyk

2010

2010 IEEE 18th International Conference on Program Comprehension

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Abstract-Data fusion is the process of integrating multiple sources of information such that their combination yields better results than if the data sources are used individually. This paper applies the idea of data fusion to feature location, the process of identifying the source code that implements specific functionality in software. A data fusion model for feature location is presented which defines new feature location techniques based on combining information from textual, dynamic, and web mining analyses applied to software. A novel contribution of the proposed model is the use of advanced web mining algorithms to analyze execution information during feature location. The results of an extensive evaluation indicate that the new feature location techniques based on web mining improve the effectiveness of existing approaches by as much as 62%.

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“…Revelle et al [15] reported that the authors' and several students' validation results agreed over 90% of the time for a certain feature. The observation is promising but not generalizable because it is a single case and the authors have identified only true positives in the results rather than the complete implementation of features.…”

Section: Related Workmentioning

confidence: 99%

“…The best analysis automatically identified 41-75% of methods modified for a feature request. Revelle et al [15] manually evaluated results of several automated feature location techniques. They reported that the best technique identified three relevant methods among the top ten methods.…”

Section: Related Workmentioning

confidence: 99%

On the effectiveness of accuracy of automated feature location technique

Ishio

Hayashi

Kawamura³

et al. 2013

2013 20th Working Conference on Reverse Engineering (WCRE)

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Abstract-Automated feature location techniques have been proposed to extract program elements that are likely to be relevant to a given feature. A more accurate result is expected to enable developers to perform more accurate feature location. However, several experiments assessing traceability recovery have shown that analysts cannot utilize an accurate traceability matrix for their tasks. Because feature location deals with a certain type of traceability links, it is an important question whether the same phenomena are visible in feature location or not. To answer that question, we have conducted a controlled experiment. We have asked 20 subjects to locate features using lists of methods of which the accuracy is controlled artificially. The result differs from the traceability recovery experiments. Subjects given an accurate list would be able to locate a feature more accurately. However, subjects could not locate the complete implementation of features in 83% of tasks. Results show that the accuracy of automated feature location techniques is effective, but it might be insufficient for perfect feature location.

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An exploratory study on assessing feature location techniques

Abstract: Abstract

Cited by 41 publications

References 23 publications

Integrating conceptual and logical couplings for change impact analysis in software

Integrating conceptual and logical couplings for change impact analysis in software

Using Data Fusion and Web Mining to Support Feature Location in Software

On the effectiveness of accuracy of automated feature location technique

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