Assessing traceability of software engineering artifacts

Sundaram, Senthil Karthikeyan; Hayes, Jane Huffman; Dekhtyar, Alex; Holbrook, E. Ashlee

doi:10.1007/s00766-009-0096-6

Cited by 64 publications

(46 citation statements)

References 34 publications

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“…Early work in this area has focused on requirements satisfaction [86] and the use of ontology to support the tracing process [41]. Second, the relation between a traced artifact and other artifacts of the same type can be analyzed in order to draw conclusions about the consistency and conclusiveness of existing and missing traces.…”

Section: Toward Automating Link Evaluationmentioning

confidence: 99%

Software traceability: trends and future directions

Cleland‐Huang

Gotel²,

Hayes

et al. 2014

Future of Software Engineering Proceedings

231

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Software traceability is a sought-after, yet often elusive quality in software-intensive systems. Required in safety-critical systems by many certifying bodies, such as the USA Federal Aviation Authority, software traceability is an essential element of the software development process. In practice, traceability is often conducted in an ad-hoc, after-the-fact manner and, therefore, its benefits are not always fully realized. Over the past decade, researchers have focused on specific areas of the traceability problem, developing more sophisticated tooling, promoting strategic planning, applying information retrieval techniques capable of semi-automating the trace creation and maintenance process, developing new trace query languages and visualization techniques that use trace links, and applying traceability in specific domains such as Model Driven Development, product line systems, and agile project environments. In this paper, we build upon a prior body of work to highlight the state-of-the-art in software traceability, and to present compelling areas of research that need to be addressed.

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Section: Toward Automating Link Evaluationmentioning

confidence: 99%

Software traceability: trends and future directions

Cleland‐Huang

Gotel²,

Hayes

et al. 2014

Future of Software Engineering Proceedings

231

View full text Add to dashboard Cite

show abstract

“…the ability to retrieve correct links) of 100%. The use of MAP as a traceability measure has been advocated in numerous papers [22], [23]. First the AP of each query is computed:…”

Section: B Evaluation Metricsmentioning

confidence: 99%

Learning effective query transformations for enhanced requirements trace retrieval

Dietrich

Cleland‐Huang

Shin

2013

2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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In automated requirements traceability, significant improvements can be realized through incorporating user feedback into the trace retrieval process. However, existing feedback techniques are designed to improve results for individual queries. In this paper we present a novel technique designed to extend the benefits of user feedback across multiple trace queries. Our approach, named Trace Query Transformation (TQT), utilizes a novel form of Association Rule Mining to learn a set of query transformation rules which are used to improve the efficacy of future trace queries. We evaluate TQT using two different kinds of training sets. The first represents an initial set of queries directly modified by human analysts, while the second represents a set of queries generated by applying a query optimization process based on initial relevance feedback for trace links between a set of source and target documents. Both techniques are evaluated using requirements from the WorldVista Healthcare system, traced against certification requirements for the Commission for Healthcare Information Technology. Results show that the TQT technique returns significant improvements in the quality of generated trace links.

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“…Sundaram S, Hayes JH, Dekhtyar A, Holbrook A [10] states the generation of traceability links or traceability matrices is vital to many software engineering activities. It is also person-power intensive, time-consuming, error-prone, and lacks tool support.…”

Section: Literature Surveymentioning

confidence: 99%