Improving After-the-Fact Tracing and Mapping: Supporting Software Quality Predictions

Hayes, Jane Huffman; Dekhtyar, Alex; Sundaram, Senthil Karthikeyan

doi:10.1109/ms.2005.156

Cited by 37 publications

(27 citation statements)

References 7 publications

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“…F1: a combination of precision and recall. We also use the levels of "goodness" for precision and recall defined in (Hayes et al, 2005) as a reference to analyse these metrics. The levels are:…”

Section: Designmentioning

confidence: 99%

Towards Effective SysML Model Reuse

Mendieta

Lloréns

Álvarez-Rodríguez

2017

Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development

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Abstract:The Systems Modeling Language (SysML) is spreading very fast. Most modelling tool vendors support it and practitioners have adopted it for Systems Engineering. The number of SysML models is growing, increasing the need for and the potential benefit from platforms that allow a user to reuse the knowledge represented in the models. However, SysML model reuse remains challenging. Each tool has its own implementation of SysML, hindering reuse between tools. The search capabilities of most tools are also very limited and finding reusable models can be difficult. This paper presents our vision and initial work towards enabling an effective reuse of the knowledge contained in SysML models. The proposed solution is based on a universal information representation model called RSHP and on existing technology for indexing and retrieval. The solution has been used to index models of all SysML diagram types and preliminary validated with requirements diagrams. The results from the validation show that the solution has very high precision and recall. This makes us confident that the solution can be a suitable means for effective SysML model reuse.

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Section: Designmentioning

confidence: 99%

Towards Effective SysML Model Reuse

Mendieta

Lloréns

Álvarez-Rodríguez

2017

Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development

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“…In the Vector Space IR model, the similarity between two documents is calculated as the cosine of the angle between two vectors D1 and D2, where the elements of D1 are the weights of the vocabulary terms in the first document and D2 the weights of the terms in the second document [12]. Generated links are usually evaluated in terms of precision ("the number of correct retrieved links (C) divided by C plus the number of retrieved false positives" [11]) and recall ("The number of correct retrieved links (C) divided by C plus the number of correct missed links" [11]). …”

Section: Background and Motivation 21 Traceabilitymentioning

confidence: 99%

Towards a benchmark for traceability

Charrada

Caspar

Jeanneret

et al. 2011

Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th Annual ERCIM Workshop on Softwar

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Rigorously evaluating and comparing traceability link generation techniques is a challenging task. In fact, traceability is still expensive to implement and it is therefore difficult to find a complete case study that includes both a rich set of artifacts and traceability links among them. Consequently, researchers usually have to create their own case studies by taking a number of existing artifacts and creating traceability links for them. There are two major issues related to the creation of one's own example. First, creating a meaningful case study is time consuming. Second, the created case usually covers a limited set of artifacts and has a limited applicability (e.g., a case with traces from high-level requirements to low-level requirements cannot be used to evaluate traceability techniques that are meant to generate links from documentation to source code). We propose a benchmark for traceability that includes all artifacts that are typically produced during the development of a software system and with end-to-end traceability linking. The benchmark is based on an irrigation system that was elaborated in a book about software design. The main task considered by the benchmark is the generation of traceability links among different types of software artifacts. Such a traceability benchmark will help advance research in this field because it facilitates the evaluation and comparison of traceability techniques and makes the replication of experiments an easy task. As a proof of concept we used the benchmark to evaluate the precision and recall of a link generation technique based on the vector space model. Our results are comparable to those obtained by other researchers using the same technique. ABSTRACTRigorously evaluating and comparing traceability link generation techniques is a challenging task. In fact, traceability is still expensive to implement and it is therefore difficult to find a complete case study that includes both a rich set of artifacts and traceability links among them. Consequently, researchers usually have to create their own case studies by taking a number of existing artifacts and creating traceability links for them. There are two major issues related to the creation of one's own example. First, creating a meaningful case study is time consuming. Second, the created case usually covers a limited set of artifacts and has a limited applicability (e.g., a case with traces from high-level requirements to low-level requirements cannot be used to evaluate traceability techniques that are meant to generate links from documentation to source code). We propose a benchmark for traceability that includes all artifacts that are typically produced during the development of a software system and with end-to-end traceability linking. The benchmark is based on an irrigation system that was elaborated in a book about software design. The main task considered by the benchmark is the generation of traceability links among different types of software artifacts. Such a traceability benchmark w...

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“…3.1.1) [15,16,20], latent semantic indexing (LSI) (see Sect. 3.1.2) [13,40], and so-called keyword extraction methods [19].…”

Section: Vocabulary Basementioning

confidence: 99%

Assessing traceability of software engineering artifacts

et al. 2010

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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. The activities that require traceability information include, but are not limited to, risk analysis, impact analysis, criticality assessment, test coverage analysis, and verification and validation of software systems. Information Retrieval (IR) techniques have been shown to assist with the automated generation of traceability links by reducing the time it takes to generate the traceability mapping. Researchers have applied techniques such as Latent Semantic Indexing (LSI), vector space retrieval, and probabilistic IR and have enjoyed some success. This paper concentrates on examining issues not previously widely studied in the context of traceability: the importance of the vocabulary base used for tracing and the evaluation and assessment of traceability mappings and methods using secondary measures. We examine these areas and perform empirical studies to understand the importance of each to the traceability of software engineering artifacts.

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Improving After-the-Fact Tracing and Mapping: Supporting Software Quality Predictions

Abstract: ■ performing traceability analysis (Do all low-level elements have parents?),

Cited by 37 publications

References 7 publications

Towards Effective SysML Model Reuse

Towards Effective SysML Model Reuse

Towards a benchmark for traceability

Assessing traceability of software engineering artifacts

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