Source Code Level Word Embeddings in Aiding Semantic Test-to-Code Traceability

Csuvik, Viktor; Kicsi, András; Vidács, László

doi:10.1109/sst.2019.00016

Cited by 21 publications

(33 citation statements)

References 25 publications

(29 reference statements)

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“…Since overly abundant recommendations can result in a high number of false matches which can diminish the usefulness of the information itself, we restricted the consideration to only the 5 most similar classes in each case, keeping the technique as simple as possible. In our current work we used our previously available tools for the generation of LSI-based similarities [15,5].…”

Section: Overviewmentioning

confidence: 99%

“…In Figure 2 one can observe the projects, on which we evaluated our technique. We used the exact same versions of the referenced projects in our previous work [15,5]. The choice of the projects was influenced by several factors: (1) the projects should be publicly available, so we could obtain the source code (2) proper naming convention were followed to some extent (for further information see Section 5), and (3) we refer to our previous work, where we used these projects and found they are good representatives for the evaluation.…”

Section: Data Collectionmentioning

confidence: 99%

“…From these projects, we obtained three different textual representations to measure similarity. We also described these representations in more detail in our previous work [5]. Similar representations are widely used in other research experiments, such as [38,40].…”

Section: Data Collectionmentioning

confidence: 99%

“…Traceability is a well-researched area with a serious industrial background. While the most widespread problem in this field is domain requirement traceability [3,22], test-to-code traceability also gained attention from the research community [35,15,5].…”

Section: Introductionmentioning

confidence: 99%

“…While several attempts have already been made to cope with this problem, these techniques are limited since they typically depend on intuitive features. In our previous work [15,5] we provided a method, that automatically links test cases and production classes relying only on conceptual information. In the current paper, we make an attempt to improve our former results by involving new machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Evaluation of Textual Similarity Techniques in Code Level Traceability

Csuvik

Kicsi

Vidács

2019

Computational Science and Its Applications – ICCSA 2019

Self Cite

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Automatic recovery of test-to-code traceability links is an important task in many areas of software engineering, like quality assurance and code maintenance. The research community has shown great interest in such a topic and has developed several techniques that already made significant advances in the field. These techniques include text-based learning algorithms, of which corpus is built from the source code of the software components. Several techniques based on information retrieval have been benchmarked, but the capabilities of many learning algorithms have not yet been tested. In this work we examine the textual similarity measures produced by three different machine learning techniques for the recovery of traceability information while also considering various textual representations of the source code. The obtained results are evaluated on 4 open source systems based on naming conventions. We have been able to improve the current textual similarity based state-of-the-art results in the case of each evaluated system.

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Section: Overviewmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of Textual Similarity Techniques in Code Level Traceability

Csuvik

Kicsi

Vidács

2019

Computational Science and Its Applications – ICCSA 2019

Self Cite

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Visualizing Multilevel Test-to-Code Relations

Aljawabrah

Qusef

Gergely

et al. 2021

Communications in Computer and Information Science

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TCTracer: Establishing test-to-code traceability links using dynamic and static techniques

White

Krinke

2022

Empir Software Eng

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Test-to-code traceability links model the relationships between test artefacts and code artefacts. When utilised during the development process, these links help developers to keep test code in sync with tested code, reducing the rate of test failures and missed faults. Test-to-code traceability links can also help developers to maintain an accurate mental model of the system, reducing the risk of architectural degradation when making changes. However, establishing and maintaining these links manually places an extra burden on developers and is error-prone. This paper presents TCTracer, an approach and implementation for the automatic establishment of test-to-code traceability links. Unlike existing work, TCTracer operates at both the method level and the class level, allowing us to establish links between tests and functions, as well as between test classes and tested classes. We improve over existing techniques by combining an ensemble of new and existing techniques that utilise both dynamic and static information and exploiting a synergistic flow of information between the method and class levels. An evaluation of TCTracer using five large, well-studied open source systems demonstrates that, on average, we can establish test-to-function links with a mean average precision (MAP) of 85% and test-class-to-class links with an MAP of 92%.

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Source Code Level Word Embeddings in Aiding Semantic Test-to-Code Traceability

Cited by 21 publications

References 25 publications

Evaluation of Textual Similarity Techniques in Code Level Traceability

Evaluation of Textual Similarity Techniques in Code Level Traceability

Visualizing Multilevel Test-to-Code Relations

TCTracer: Establishing test-to-code traceability links using dynamic and static techniques

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