On the Use of Domain Terms in Source Code

Haiduc, Sonia; Marcus, Andrian

doi:10.1109/icpc.2008.29

Cited by 65 publications

(45 citation statements)

References 23 publications

(16 reference statements)

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“…These names often serve as a starting point in many program comprehension tasks (Caprile and Tonella 1999;Haiduc and Marcus 2008;Abebe et al 2009;Arnaoudova et al 2010). Hence, it is essential that these names clearly reflect the concepts that they are supposed to represent, as self-documenting identifiers decrease the time and effort needed to acquire a basic comprehension level for a programming task Binkley et al 2009).…”

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 authors' observations on the missing links was: (1) it was difficult to do some of the tracing because the documents/requirements were incomplete, ambiguous and (2) unknown acronyms hindered the trace recovery process. Haiduc and Marcus [Haiduc and Marcus(2008)] studied several open-source systems and found that about 40% of the domain terms are being used in the source code by developers. If the domain terms are ambiguous, it will also impact source code as well.…”

Section: Ambiguous Requirementsmentioning

confidence: 99%

Factors Impacting the Inputs of Traceability Recovery Approaches

Ali

Guéhéneuc

Antoniol

2011

Software and Systems Traceability

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In requirement engineering, researchers have proposed various tractability recovery approaches. To the best of our knowledge, all traceability recovery approaches have low precision and recall. Our main claim in this chapter is that there exist factors that impact the traceability approaches' inputs, in particular source document, target document, and experts' opinion, that cause low precision and recall. In this chapter, we pursue four objectives: first, to identify and document factors that impact traceability recovery approaches' inputs; second, to identify metrics/tools to measure/improve the quality of the inputs with respect to the identified factors, third, to provide precautions to control these factors, and, fourth, to empirically prove and quantify the effect of one of these factors-expert's programming knowledge-on the traceability recovery approaches' inputs. To achieve the first two objectives, we perform an incremental literature review of traceability recovery approaches and identify and document three key inputs and the seven factors impacting these inputs, out of 12 identified factors. We analyse the reported results in literature for the identified factors to address our third objective. We conduct an empirical study to assess the impact of expert's programming knowledge, to address our fourth objective. We use the effort, number of correct answers, and time to measure the effect of expert's programming knowledge on traceability recovery. We conclude that, in the literature, seven factors impacting the inputs of traceability recovery approaches have been identified, documented, and reported along with related metrics/tools and precautions. We suggest that practitioners should be wary of these seven factors and researchers should focus on the five others to improve traceability recovery approaches.

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“…Therefore, we assume that two code fragments that contain identifiers belonging to the same concept might provide the same functionality and could, therefore, be potential re-implementations. This assumption is strengthened by Haiduc and Marcus [13]. …”

Section: General Approachmentioning

confidence: 87%

A Novel Approach to Detect Unintentional Re-implementations

Bauer

Jürgens²

2014

2014 IEEE International Conference on Software Maintenance and Evolution

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Abstract-Unintentional re-implementation of existing functionality is an issue frequently reported in practice and causes increased efforts for development and maintenance. However, instances are hard to find with existing approaches. For practitioners, this increases maintenance risks, such as inconsistent bug fixing, and hinders quality improvement efforts. For researchers, this hinders a reliable quantification of the issue.We propose a pragmatic approach combining identifierbased concept location with static analysis to detect candidate re-implementations between two sets of source code. We present initial results from applying the approach to detect reimplementations of utility functionality present in libraries within a sample of Java projects.

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On the Use of Domain Terms in Source Code

Cited by 65 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

Factors Impacting the Inputs of Traceability Recovery Approaches

A Novel Approach to Detect Unintentional Re-implementations

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