Mind the gap: assessing the conformance of software traceability to relevant guidelines

Rempel, Patrick; Mäder, Patrick; Kuschke, Tobias; Cleland‐Huang, Jane

doi:10.1145/2568225.2568290

Cited by 54 publications

(30 citation statements)

References 38 publications

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“…Such guidelines often specify traceability paths (i.e. that trace links must be established between particular types of source artifacts and target artifacts), and they often also specify the purpose of establishing the link [80]. What they fail to do is to specify the granularity of the links and the optimal trace path (i.e.…”

Section: Traceability Guidelinesmentioning

confidence: 99%

“…Recent work by Rempel et al generated a TIM from traceability data in a number of safety-critical projects and used formal logic to compare it to the TIM prescribed by relevant process guidelines. They formally defined a number of potential inconsistency and compliance problems and identified the occurrence of such problems in each of the evaluated projects [80]. However, other metrics could be designed to measure factors such as completeness, quality, and/or coverage of existing trace links.…”

Section: Determining and Communicating Confidencementioning

confidence: 99%

See 1 more Smart Citation

Software traceability: trends and future directions

Cleland‐Huang

Gotel²,

Hayes

et al. 2014

Future of Software Engineering Proceedings

Self Cite

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: Traceability Guidelinesmentioning

confidence: 99%

Section: Determining and Communicating Confidencementioning

confidence: 99%

Software traceability: trends and future directions

Cleland‐Huang

Gotel²,

Hayes

et al. 2014

Future of Software Engineering Proceedings

Self Cite

231

View full text Add to dashboard Cite

show abstract

“…We characterize the required traceability information via the traceability information model (TIM) of Figure 5. TIMs are a common way of specifying how different development artifacts (and the elements thereof) should be traced to one another in order to support specific analytical tasks [12,13,14].…”

Section: Building Sysml Modelsmentioning

confidence: 99%

Automated change impact analysis between SysML models of requirements and design

Nejati

Sabetzadeh

Arora

et al. 2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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An important activity in systems engineering is analyzing how a change in requirements will impact the design of a system. Performing this analysis manually is expensive, particularly for complex systems. In this paper, we propose an approach to automatically identify the impact of requirements changes on system design, when the requirements and design elements are expressed using models. We ground our approach on the Systems Modeling Language (SysML) due to SysML's increasing use in industrial applications.Our approach has two steps: For a given change, we first apply a static slicing algorithm to extract an estimated set of impacted model elements. Next, we rank the elements of the resulting set according to a quantitative measure designed to predict how likely it is for each element to be impacted. The measure is computed using Natural Language Processing (NLP) applied to the textual content of the elements. Engineers can then inspect the ranked list of elements and identify those that are actually impacted. We evaluate our approach on an industrial case study with 16 real-world requirements changes. Our results suggest that, using our approach, engineers need to inspect on average only 4.8% of the entire design in order to identify the actually-impacted elements. We further show that our results consistently improve when our analysis takes into account both structural and behavioral diagrams rather than only structural ones, and the natural-language content of the diagrams in addition to only their structural and behavioral content.

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“…Some safety domains even developed domain-specific guidelines (e.g., avionics: , space: [ECSS 2009], automotive: [ISO 26262-6:2011], railway: [CENELEC 2011], medical: [FDA 2002) to address their particular needs. Although these guidelines were created to support practitioners, organizations in practice struggle to implement accurate and complete sets of trace links [Rempel et al 2013;Mäder et al 2013;Rempel et al 2014]. An analysis of the traceability information submitted by various organizations to the US Food and Drug Administration (FDA) as part of the medical device approval process in the United States showed a significant traceability gap between the traceability expectations as laid out in the FDA's "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" [FDA 2002], and the traceability data documented in the submissions .…”

Section: Motivationmentioning

confidence: 99%