Exploring the neighborhood with dora to expedite software maintenance

Hill, Emily; Pollock, Lori; Vijay‐Shanker, K.

doi:10.1145/1321631.1321637

Cited by 95 publications

(103 citation statements)

References 52 publications

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“…More recent work appears in (Robillard 2005;Hill et al 2007;Saul et al 2007;Robillard 2008), where proposed tools could help navigate and prioritize system dependencies during various software maintenance tasks. The work in (Hill et al 2007) relates to our approach only to the extent that it also uses lexical (textual) clues from the source code to identify related methods.…”

Section: Software Change Impact Analysis (Ia)mentioning

confidence: 99%

“…The work in (Hill et al 2007) relates to our approach only to the extent that it also uses lexical (textual) clues from the source code to identify related methods. Several recent papers presented algorithms that estimate the impact of a change on tests (Rountev et al 2001;Kosara et al 2003).…”

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: 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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“…Researchers have recognized that combining more than one approach to feature location can produce better results than standalone techniques [11,13,18,22,25,33]. Generally in these combined approaches, information from one source is used to filter results from another.…”

Section: Introductionmentioning

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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Can method data dependencies support the assessment of traceability between requirements and source code?

Kuang

Mäder

et al. 2015

J. Softw. Evol. and Proc.

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Requirements traceability benefits many software engineering activities, such as change impact analysis and risk assessment. However, these activities require complete and correct traceability links which is not trivial, making traceability assessment an important field of study. In recent years, requirements traceability research has focused on using call dependencies within source code to understand how code properties contribute to the implementation of a requirement and to assess whether traceability links are correct and complete. These approaches largely ignore the role of existing data dependencies within the source code. That is, methods may never call each other, but may still depend upon another by sharing data. We identified five research questions and validated them on five software systems, covering 4 to 72 KLOC. We found that data dependencies are as relevant as call dependencies for assessing requirements traceability. Even more interesting, our analyses show that data dependencies complement call dependencies in the assessment. These findings have strong implications on code understanding, including trace capture, maintenance, and validation techniques.

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Exploring the neighborhood with dora to expedite software maintenance

Cited by 95 publications

References 52 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

Can method data dependencies support the assessment of traceability between requirements and source code?

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