Support for traceability management of software artefacts using Natural Language Processing

Arunthavanathan, A.; Shanmugathasan, S.; Ratnavel, S.; Thiyagarajah, V.; Perera, Indika; Meedeniya, Dulani; Balasubramaniam, Dharini

doi:10.1109/mercon.2016.7480109

Cited by 14 publications

(9 citation statements)

References 4 publications

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“…CICD can be applied to straightforwardly accomplish such requirements as in DevOps. In addition, the design and development of supporting tools to automate the MLOps process can be extended by incorporating natural language processing (NLP) as well [24]. The health of the models should be live monitored and precautions should be taken to reduce the impact on the production application.…”

Section: Current Challenges and Future Research Directionsmentioning

confidence: 99%

Machine Learning Operations: A Survey on MLOps Tool Support

Hewage¹,

Meedeniya²

2022

Preprint

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Machine Learning (ML) has become a fast-growing, trending approach in solution development in practice. Deep Learning (DL) which is a subset of ML, learns using deep neural networks to simulate the human brain. It trains machines to learn techniques and processes individually using computer algorithms, which is also considered to be a role of Artificial Intelligence (AI). In this paper, we study current technical issues related to software development and delivery in organizations that work on ML projects. Therefore, the importance of the Machine Learning Operations (MLOps) concept, which can deliver appropriate solutions for such concerns, is discussed. We investigate commercially available MLOps tool support in software development. The comparison between MLOps tools analyzes the performance of each system and its use cases. Moreover, we examine the features and usability of MLOps tools to identify the most appropriate tool support for given scenarios. Finally, we recognize that there is a shortage in the availability of a fully functional MLOps platform on which processes can be automated by reducing human intervention.

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Section: Current Challenges and Future Research Directionsmentioning

confidence: 99%

Machine Learning Operations: A Survey on MLOps Tool Support

Hewage¹,

Meedeniya²

2022

Preprint

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“…Most of the time, the textual content in the artefacts provides descriptive details about its informal semantics. The frequently involved pre-processing steps for textual-based requirements artefacts are Natural Language Processing (NLP) tasks such as tokenization, text normalization, anaphora analysis, morphological analysis and stemming [4] [21]. It is assumed that the artefacts are conceptually related if their textual contents are similar Thus, trace links can be created among them.…”

Section: A Information Retrieval and Data Pre-processingmentioning

confidence: 99%

“…The tool Software Artefact Traceability Analyser (SAT-Analyser) [21][48] has addressed the traceability among requirements artefact, UML class diagrams regarding the design artefact and the source code artefact in Java programming language. It has used NLP and traceability has been established based on a string similarity computation using the Jaro-Winkler algorithm and Levenshtein Distance algorithm along with WordNet synonyms and pre-defined dictionary ontology.…”

Section: B Related Studies On Traceability Managementmentioning

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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“…Alobaidi and Mahmood [73] found the relationship between requirement documents and others using semantic similarity and semantic relatedness. Arunthavanathan et al [75] used the NLP techniques to convert the natural language requirement document into XML format by extracting classes, methods, attributes and relationships and then used the XML format so generated in their tool ''Software Artefacts Traceability Analyser (SAT),'' which resolved the problem of artefacts traceability, that is, links between the artefacts like Requirements, UML Class Diagrams and Java code of the project, when a change occurs. Le et al [15] proposed the approach called RCLinker, where they found the link between the issue reports and the commit messages by considering the context along with comparing the text between these two artefacts.…”

Section: Traceabilitymentioning

confidence: 99%

Natural language processing in mining unstructured data from software repositories: a review

Gupta

2019

Sādhanā

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With the increasing popularity of open-source platforms, software data is easily available from various open-source tools like GitHub, CVS, SVN, etc. More than 80 percent of the data present in them is unstructured. Mining data from these repositories helps project managers, developers and businesses, in getting interesting insights. Most of the software artefacts present in these repositories are in the natural language form, which makes natural language processing (NLP) an important part of mining to get the useful results. The paper reviews the application of NLP techniques in the field of Mining Software Repositories (MSR). The paper mainly focuses on sentiment analysis, summarization, traceability, norms mining and mobile analytics. The paper presents the major NLP works performed in this area by surveying the research papers from 2000 to 2018. The paper firstly describes the major artefacts present in the software repositories where the NLP techniques have been applied. Next, the paper presents some popular open-source NLP tools that have been used to perform NLP tasks. Later the paper discusses, in brief, the research state of NLP in MSR field. The paper also lists down the various challenges along with the pointers for future work in this field of research and finally the conclusion.

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Support for traceability management of software artefacts using Natural Language Processing

Cited by 14 publications

References 4 publications

Machine Learning Operations: A Survey on MLOps Tool Support

Machine Learning Operations: A Survey on MLOps Tool Support

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

Natural language processing in mining unstructured data from software repositories: a review

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