Variable granularity for improving precision of impact analysis

Petrenko, Maksym; Rajlich, Václav

doi:10.1109/icpc.2009.5090023

Cited by 70 publications

(55 citation statements)

References 28 publications

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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%

“…A typical IA technique takes a software entity in which a change is proposed or identified, and estimates other entities that are also potential change candidates, referred to as an estimated impact set (Zimmermann et al 2005;Hill et al 2007;Petrenko and Rajlich 2009;Poshyvanyk et al 2009;Kagdi et al 2010). Our general approach computes the estimated impact set with the following steps:…”

Section: A Combined Approach To Impact Analysismentioning

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: A Combined Approach To Impact Analysismentioning

confidence: 99%

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 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]). Since software systems may contain thousands of classes, e.g., Mozilla or Eclipse, focusing impact analysis on classes, which are strongly coupled to a starting point, may provide valuable automated support to the developer.…”

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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“…In a similar way to HD, complex dependencies are captured by semantic information which is hard to detect by traditional program analysis techniques (Vanciu and Rajlich 2010). Some CIA tools do not discover HD, and it is the responsibility of the programmer to correctly identify and trace HD during change impact analysis (Petrenko and Rajlich 2009).…”

Section: Introductionmentioning

confidence: 99%

An empirical study on the interplay between semantic coupling and co-change of software classes

2017

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Software systems continuously evolve to accommodate new features and interoperability relationships between artifacts point to increasingly relevant software change impacts. During maintenance, developers must ensure that related entities are updated to be consistent with these changes. Studies in the static change impact analysis domain have identified that a combination of source code and lexical information outperforms using each one when adopted independently. However, the extraction of lexical information and the measure of how loosely or closely related two software artifacts are, considering the semantic information embedded in their comments and identifiers has been carried out using somewhat complex information retrieval (IR) techniques. The interplay between software semantic and change relationship strengths has also not been extensively studied. This work aims to fill both gaps by comparing the effectiveness of measuring semantic coupling of OO software classes using (i) simple identifier based techniques and (ii) the word corpora of the entire classes in a software system. Afterwards, we empirically investigate the interplay between semantic and change coupling. The empirical results show that: (1) identifier based methods have more computational efficiency but cannot always be used interchangeably with corpora-based methods of computing semantic coupling of classes and (2) there is no correlation between semantic and change coupling. Furthermore we found that (3) there is a directional relationship between the two, as over 70% of the semantic dependencies are also linked by change coupling but not vice versa.

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Variable granularity for improving precision of impact analysis

Abstract: Abstract

Cited by 70 publications

References 28 publications

Integrating conceptual and logical couplings for change impact analysis in software

Integrating conceptual and logical couplings for change impact analysis in software

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

An empirical study on the interplay between semantic coupling and co-change of software classes

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