Whole program path-based dynamic impact analysis

Law, James; Rothermel, Gregg

doi:10.1109/icse.2003.1201210

Cited by 181 publications

(225 citation statements)

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“…Traceability analysis refers to impact analysis of software artifacts across different levels of abstractions (e.g., source code to UML). Various dependency-analysis methods based on call graphs, program slicing (Gallagher and Lyle 1991), hidden dependency analysis (Rajlich 1997;Chen and Rajlich 2001;Yu and Rajlich 2001), lightweight static analysis approaches (Moonen 2002;Petrenko and Rajlich 2009), concept analysis (Tonella 2003), dynamic analysis (Law and Rothermel 2003;Orso et al 2004;Ren et al 2004), hypertext systems, documentation systems, UML models (Briand et al 2002), and Information Retrieval (Antoniol et al 2000;) are already investigated in the literature. Queille et al (Queille et al 1994) proposed an interactive process in which the programmer, guided by dependencies among program components (i.e., classes, functions), inspects components one-by-one and identifies the ones that are going to change -this process involves both searching and browsing activities.…”

Section: Software Change Impact Analysis (Ia)mentioning

confidence: 99%

“…Decades of research efforts have produced a wide spectrum of approaches, ranging from the traditional static and dynamic analysis techniques Law and Rothermel 2003;Orso et al 2004;Ren et al 2004;Robillard 2005;Petrenko and Rajlich 2009) to the contemporary methods such as those based on Information Retrieval (IR) (Canfora and Cerulo 2005;Hill et al…”

Section: Introductionmentioning

confidence: 99%

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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: Software Change Impact Analysis (Ia)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating conceptual and logical couplings for change impact analysis in software

2012

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“…Bohner and Arnold surveyed IA methodologies in 1996 [1], and a number of approaches based on improved static and dynamic analyses are proposed thereafter (e.g., [2,3,13,16,17]). An extensive examination of the MSR approaches was recently completed [9], of which a preliminary survey of six approaches that support software changes is discussed in [10].…”

Section: Introductionmentioning

confidence: 99%

Combining Single-Version and Evolutionary Dependencies for Software-Change Prediction

Kagdi

Maletic

2007

Fourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007)

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“…Bohner and Arnold surveyed IA methodologies in 1996 [2], and a number of approaches based on improved static and dynamic analyses are proposed thereafter [1,10,16]. Our general approach consists of the following steps:…”

Section: B Change Impact Analysis (Ia)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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Whole program path-based dynamic impact analysis

Abstract: Abstract

Cited by 181 publications

References 20 publications

Integrating conceptual and logical couplings for change impact analysis in software

Integrating conceptual and logical couplings for change impact analysis in software

Combining Single-Version and Evolutionary Dependencies for Software-Change Prediction

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

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