XTraQue: traceability for product line systems

Jirapanthong, Waraporn; Zisman, Andrea

doi:10.1007/s10270-007-0066-8

Cited by 63 publications

(53 citation statements)

References 44 publications

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“…It is potentially cost-e↵ective to reuse trace links across product lines. Several authors have explored the use of traceability to support product line evolution [1], generate trace links across artifacts in a product line [47,2], and to connect features to artifacts [69]. Given the rapid adoption of product lines in industry, it is essential to investigate and mature this work.…”

Section: Evolving Linksmentioning

confidence: 99%

Software traceability: trends and future directions

Cleland‐Huang

Gotel²,

Hayes

et al. 2014

Future of Software Engineering Proceedings

Self Cite

233

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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: Evolving Linksmentioning

confidence: 99%

Software traceability: trends and future directions

Cleland‐Huang

Gotel²,

Hayes

et al. 2014

Future of Software Engineering Proceedings

Self Cite

233

View full text Add to dashboard Cite

show abstract

“…Sundaram, Hayes, and Dekhtyar] 7.9 75.6 4.2 -6.3 63.4 -92.6 ---CM-1 Sundaram, Hayes, and Dekhtyar] 1.5 97.7 0.9 98.6 -98.8 ---Easy Clinic [Oliveto et al(2010) Oliveto, Gethers, Poshyvanyk, and De Lucia] 17 -80 4 -90 17 -60 3 -90 17 -80 4 -91 -eTour [Oliveto et al(2010) Oliveto, Gethers, Poshyvanyk, and De Lucia] 17 -68 5 -47 17 -64 4 -46 17 -76 5 -47 -Mobile Phone [Jirapanthong and Zisman(2007)] ------81 -95.9 6 UCMS, TV Software [Spanoudakis et al(2004)Spanoudakis, Zisman, Pérez-Minana, and Krause] ------60 -81…”

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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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“…Alferez et al [5] provide a trace metamodel to capture traces between feature models and use cases. XTraQue [20] is a tool which supports semi-automatic trace generation for use cases and feature models. Despite advances in traceability research, all approaches given above bring additional modeling and traceability effort into practice.…”

Section: Related Workmentioning

confidence: 99%

Applying product line Use case modeling in an industrial automotive embedded system: Lessons learned and a refined approach

Hajri

Göknil

Briand

et al. 2015

2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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Abstract-In this paper, we propose, apply, and assess Product line Use case modeling Method (PUM), an approach that supports modeling variability at different levels of granularity in use cases and domain models. Our motivation is that, in many software development environments, use case modeling drives interactions among stakeholders and, therefore, use cases and domain models are common practice for requirements elicitation and analysis. In PUM, we integrate and adapt existing product line extensions for use cases and introduce some template extensions for use case specifications. Variability is captured in use case diagrams while it is reflected at a greater level of detail in use case specifications. Variability in domain concepts is captured in domain models. PUM is supported by a tool relying on Natural Language Processing (NLP). We successfully applied PUM to an industrial automotive embedded system and report lessons learned and results from structured interviews with experienced engineers.

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XTraQue: traceability for product line systems

Cited by 63 publications

References 44 publications

Software traceability: trends and future directions

Software traceability: trends and future directions

Factors Impacting the Inputs of Traceability Recovery Approaches

Applying product line Use case modeling in an industrial automotive embedded system: Lessons learned and a refined approach

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