A multi-agent K-means with case-based reasoning for an automated quality assessment of software requirement specification
Automating the quality assessment of Software Requirement Specification poses major challenges related to the need for advanced algorithms to extract the SRS quality features, interpret the context of the features, formulate accurate assessment metrics, and document the shortcomings as well as possi...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/8843/1/J15623_6f07f3ec314a1568f718a3e2a235512e.pdf http://eprints.uthm.edu.my/8843/ https://doi.org/10.1049/cmu2.12555 |
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| Summary: | Automating the quality assessment of Software Requirement Specification poses major challenges related to the need for advanced algorithms to extract the SRS quality features, interpret the context of the features, formulate accurate assessment metrics, and document the shortcomings as well as possible improvements. In the existing methods, such as Reconstructed Automated Requirement Measurement, and Rendex, some major processes are still handled offline by humans (semi-automated) or encompass automating
the measurement of a few quality attributes due to the mentioned challenges. This paper addressed this gap and proposed an Automated Quality Assessment of SRS (AQA-SRS) framework to assess the SRS documents by automatically extracting features related to 11 quality attributes through a deep analysis of the SRS textual content. Also, it constructs aflexible platform that is able to minimize the human expert’s role in the SRS assessment. The AQA-SRS framework integrates Natural Language Processing, K-means, Multi-agent, and Case-Based Reasoning. The AQA-SRS framework is evaluated by processing two standard SRS datasets and comparing the results with state-of-the-art methods and analysis by software engineering experts. The results show that the AQA-SRS framework effectively
assesses the tested SRS documents and achieves a 78% total agreement with the tested methods and software engineering experts. |
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