Software Artefact Traceability Analyser

Rubasinghe, Iresha; Meedeniya, Dulani; Perera, Indika

doi:10.1145/3193092.3193094

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…We have also identified some tools that were developed continuously across the years (covered by multiple papers reflecting incremental development) across the SDLC phases, namely Software Artifacts Traceability Analyzer (SAT-Analyzer) and TiQi. NLP was first introduced in SAT-Analyzer for addressing artifact inconsistencies due to natural language representation (Arunthavanathan et al, 2016) -it improves the usability of SAT-Analyzer through automated generation of XML input from requirement artifacts, which was then evaluated by a case study on a Pointof-sale (POS) system (Rubasinghe et al, 2018b). SAT-Analyzer was also covered in DevOps practices (Rubasinghe et al, 2018a(Rubasinghe et al, , 2020; a traceability management tool for continuous integration and multi-user collaboration.…”

Section: Continuous Developed Toolsmentioning

confidence: 99%

“…In a case study for SAT-Analyzer, it was observed that the inaccurate artifact elements extraction and identification with NLP that contain different naming conventions and less meaningful names in requirement artifacts, have led to the lack of accuracy (Rubasinghe et al, 2018b). Semantic ambiguities in artifacts written in natural language pose a challenge in tracing explicit links with other artifacts, based on the syntax used (Kchaou et al, 2019).…”

Section: Syntax Conventionmentioning

confidence: 99%

“…In a dynamic continuously integrated, continuously developing environment (Rubasinghe et al, 2018a(Rubasinghe et al, , 2020(Rubasinghe et al, , 2018b, artifacts transform constantly and this hampers continuous traceability efforts. In cases where traceability is necessary for regulations (Florez, 2019;Arora et al, 2015), the natural language used in these documents is not represented similarly to other artifacts, such as functional and non-functional requirements.…”

Section: Properties (Representation) Of Artifactsmentioning

confidence: 99%

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Applications of Natural Language Processing in Software Traceability: A Systematic Mapping Study

Pauzi

Capiluppi

2022

SSRN Journal

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Section: Continuous Developed Toolsmentioning

confidence: 99%

Section: Syntax Conventionmentioning

confidence: 99%

Section: Properties (Representation) Of Artifactsmentioning

confidence: 99%

See 1 more Smart Citation

Applications of Natural Language Processing in Software Traceability: A Systematic Mapping Study

Pauzi

Capiluppi

2022

SSRN Journal

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“…Most of the traceability visualization techniques have slightly considered model driven features; thus, there is a limit of supporting to a range of software types [36] [37]. Many related works have addressed issues such as visual clutter and scalability [32] [33] and several tools are integrated with a specific Integrated Development Environment (IDE).…”

Section: Traceability Visualizationmentioning

confidence: 99%

Traceability Establishment and Visualization of Software Artefacts in DevOps Practice: A Survey

Meedeniya¹,

Rubasinghe²,

Perera³

2019

IJACSA

Self Cite

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DevOps based software process has become popular with the vision of an effective collaboration between the development and operations teams that continuously integrates the frequent changes. Traceability manages the artefact consistency during a software process. This paper explores the trace-link creation and visualization between software artefacts, existing tool support, quality aspects and the applicability in a DevOps environment. As the novelty of this study, we identify the challenges that limit the traceability considerations in DevOps and suggest research directions. Our methodology consists of concept identification, state-of-practice exploration and analytical review. Despite the existing related work, there is a lack of tool support for the traceability management between heterogeneous artefacts in software development with DevOps practices. Although many existing studies have low industrial relevance, a few proprietary traceability tools have shown a high relevance. The lack of evidence of the related applications indicates the need for a generalized traceability approach. Accordingly, we conclude that the software artefact traceability is maturing and applying collaboratively in the software process. This can be extended to explore features such as artefact change impact analysis, change propagation, continuous integration to improve software development in DevOps environments.

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“…Graph-based visualization allows visualization of all overview of traceability links between various software artifacts. Graph can be easily used to represent the trace data [156]. For instance, nodes can represent artifacts such as classes, subsystems, objects, while the edges can represent how these artifacts can be connected (i.e.…”

Section: Graphmentioning

confidence: 99%

Towards Visualization of Unit Test and Source Code Relations

Aljawabrah

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Test-to-code traceability is the ability to relate the test unit and source code artifacts created during the software development life cycle (SDLC). Traceability of test and code relations is fundamental to support various activities of software development such as program comprehension, verification and validation, impact analysis, reuse, maintenance, and software evolution. Notwithstanding its importance, many significant challenges are associated with traceability. One of these challenges is how to support the comprehension and maintenance of these links efficiently and effectively.Visualization is an important part that effectively supports understanding test-tocode relations. It provides enhanced process visibility, helps engineers, developers, and testers to verify the quality of TCT links, and understand which code modules are tested by which unit tests.As there are many sources from where the traceability relations can be inferred, one of the most important questions is to decide which source or combination of sources is the best to determine the test-to-code links. It is obvious that, if these sources disagree, this will make it harder to understand what is going on, what was the goal of the developer, and how the components are really related and change impact analysis can yield false results, etc. Fortunately, visualization can aid this task. The presented approach consists of three parts. The first part consists of the recognition of artifacts from two different areas, source code, and unit test. The second part presents different sources for capturing the links between code and test iv such as a naming convention, last call before assert, and the static call graph. The third part includes the visualization method used to visually present the traceability links inferred from traceability links sources. The trace visualization approach is implemented as a trace visualization tool, which is called TCTracVis.This thesis also provides an empirical study based on the implementation of the presented approach. The approach and its tool support are applied in different software development projects conducted with a group of students, academics, and from industry. The effectiveness and practicability of the presented approach and its tool support have been evaluated. The effectiveness results indicate that the visualization of multilevel test-to-code traceability links, inferred from multiple sources, is more effective for the testers and developers than using visualization of a single source of traceability links. It helps to get a bigger picture of what is going on with the tests, find solutions to the problems in testing, and understand the relationships between test cases and the corresponding units under test. At the same time, the usability results indicate that the participants found that the approach and its tool support usability, and enhance the overall browsing, comprehension, and maintenance of test-to-code traceability links of a system.

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Software Artefact Traceability Analyser

Cited by 6 publications

References 16 publications

Applications of Natural Language Processing in Software Traceability: A Systematic Mapping Study

Applications of Natural Language Processing in Software Traceability: A Systematic Mapping Study

Traceability Establishment and Visualization of Software Artefacts in DevOps Practice: A Survey

Towards Visualization of Unit Test and Source Code Relations

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