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

Poshyvanyk, Denys; Guéhéneuc, Yann‐Gaël; Marcus, Andrian; Antoniol, Giuliano; Rajlich, Václav

doi:10.1109/tse.2007.1016

Cited by 395 publications

(380 citation statements)

References 42 publications

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“…In their seminal work, Wilde and Scully [24] used test cases to produce execution traces; concepts location was performed by comparing different traces: one in which the concept is executed and another without concept. Similarly, Poshyvanyk et al [19] used multiple traces from multiple scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…An important task during program comprehension is concept location, which aims at identifying concepts (e.g., domain concepts, user-observable features) and locating them within code regions or, more generally, into software artifact chunks [14,8]. The literature reports concept location approaches built upon static [1] and dynamic [24,23] analyses; information retrieval (IR) [19]; and hybrid (static and dynamic) [3,12,5] techniques. Dynamic and hybrid approaches rely on execution traces.…”

Section: Introductionmentioning

confidence: 99%

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A Fast Algorithm to Locate Concepts in Execution Traces

Medini

Galinier

Penta

et al. 2011

Search Based Software Engineering

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Abstract. The identification of cohesive segments in execution traces is a important step in concept location which, in turns, is of paramount importance for many program-comprehension activities. In this paper, we reformulate the trace segmentation problem as a dynamic programming problem. Differently to approaches based on genetic algorithms, dynamic programming can compute an exact solution with better performance than previous approaches, even on long traces. We describe the new problem formulation and the algorithmic details of our approach. We then compare the performances of dynamic programming with those of a genetic algorithm, showing that dynamic programming reduces dramatically the time required to segment traces, without sacrificing precision and recall; even slightly improving them.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fast Algorithm to Locate Concepts in Execution Traces

Medini

Galinier

Penta

et al. 2011

Search Based Software Engineering

Self Cite

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show abstract

“…For a given class C (considered to be a starting point of a change task, which may be identified by a programmer via a feature location technique, e.g., PROMESIR [34]), the remaining classes in the system are ranked according to their strength of coupling to the class C, based on a coupling measure or a combination of such measures (see some previous work on the details [8,35]). The list of ranked classes is presented to the developer for further inspection (for instance, a ranked list of classes as shown in an existing tool for impact analysis, namely JRipples [32]).…”

Section: ) Impact Analysis Using Coupling Metricsmentioning

confidence: 99%

Using Relational Topic Models to capture coupling among classes in object-oriented software systems

Gethers

Poshyvanyk

2010

2010 IEEE International Conference on Software Maintenance

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-Coupling metrics capture the degree of interaction and relationships among source code elements in software systems. A vast majority of existing coupling metrics rely on structural information, which captures interactions such as usage relations between classes and methods or execute after associations. However, these metrics lack the ability to identify conceptual dependencies, which, for instance, specify underlying relationships encoded by developers in identifiers and comments of source code classes. We propose a new coupling metric for object-oriented software systems, namely Relational Topic based Coupling (RTC) of classes, which uses Relational Topic Models (RTM), generative probabilistic model, to capture latent topics in source code classes and relationships among them. A case study on thirteen open source software systems is performed to compare the new measure with existing structural and conceptual coupling metrics. The case study demonstrates that proposed metric not only captures new dimensions of coupling, which are not covered by the existing coupling metrics, but also can be used to effectively support impact analysis.

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“…Identifying the parts of the source code that correspond to a specific functionality is a prerequisite to several maintenance tasks. This process is referred to as feature location [13] and it is a part of the incremental change [15]. For example, assume a developer is working on text editor software and needs to modify the file printing feature to ensure the files can be also printed to PDF format.…”

Section: A Feature Location (Fl)mentioning

confidence: 99%

SE<sup>2</sup> model to support software evolution

Kagdi

Gethers

Poshyvanyk

2011

2011 27th IEEE International Conference on Software Maintenance (ICSM)

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Abstract-The paper proposes an integrated approach, namely SE 2 , to support three core software maintenance and evolution tasks: feature location, software change impact analysis, and expert developer recommendation. The approach is centered on the combinations of the conceptual and evolutionary relationships latent in structured and unstructured software artifacts. Information Retrieval (IR) and Mining Software Repositories (MSR) based techniques are used for analyzing and deriving these relationships. All the three tasks are supported under a single, common framework by providing systematic combinations of MSR and IR analyses on single and multiple versions of a software system. This combining ability of SE 2 sets it apart from previously reported relevant solutions in the literature. The outlined empirical assessment is aimed at identifying the exclusive and synergistic improvements offered by such combinations for each of the addressed tasks. Preliminary evaluation on a number of open source systems suggests that such combinations do offer improvements over individual approaches.

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

Cited by 395 publications

References 42 publications

A Fast Algorithm to Locate Concepts in Execution Traces

A Fast Algorithm to Locate Concepts in Execution Traces

Using Relational Topic Models to capture coupling among classes in object-oriented software systems

SE<sup>2</sup> model to support software evolution

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