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Exploring Software Quality Through Data-Driven Approaches and Knowledge Graphs

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Good Practices and New Perspectives in Information Systems and Technologies (WorldCIST 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 990))

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Abstract

Context: The quality of software systems has always been a crucial task and has led to the establishment of various reputable software quality models. However, the automation trends in Software Engineering have challenged the traditional notion of quality assurance, motivating the development of a new paradigm with advanced AI-based quality standards.

Objective: The goal of this paper is to bridge the gap between theoretical frameworks and practical implementations on the aspects of software quality.

Methodology: This study involved an extensive literature review of software quality models, including McCall, Boehm, Dromey, FURPS, and ISO/IEC 25010. The detailed information about quality attributes from each model was systematically synthesized and organized into datasets, data frames, and Python dictionaries. The resulting resources were then shared and made accessible through a public GitHub repository.

Results: In brief, this research provides (i) a comprehensive dataset on software quality containing catalogs of quality models and attributes, (ii) a Python dictionary encapsulating the quality models and their associated characteristics for convenient empirical experimentation, (iii) the application of advanced knowledge graph techniques for the analysis and visualization of software quality parameters, and (iv) the complete construction steps and resources for download, ensuring easy integration and accessibility.

Conclusion: This study builds a foundational step towards the standardization of automating software quality modeling to enhance not just quality but also efficiency for software development. For our future work, there will be a concentration on the practical utilization of the dataset in real-world software development contexts.

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, Pakistan

    Raheela Chand

  2. Department of Computing, Shifa Tameer-e-Millat University, Park Road Campus, Islamabad, Pakistan

    Saif Ur Rehman Khan

  3. Department of Computer Science and Software Engineering, School of Engineering, Penn State University, Behrend, USA

    Shahid Hussain, Wen-Li Wang & Naseem Ibrahim

  4. Computer and Information Science, Gannon University, Pennsylvania, USA

    Mei-Huei Tang

Authors
  1. Raheela Chand
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  2. Saif Ur Rehman Khan
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  3. Shahid Hussain
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  4. Wen-Li Wang
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  5. Mei-Huei Tang
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  6. Naseem Ibrahim
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Corresponding author

Correspondence to Raheela Chand .

Editor information

Editors and Affiliations

  1. ISEG, Universidade de Lisboa, Lisbon, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

  5. Institute of Information Technology, Lodz University of Technology, Łódz, Poland

    Aneta Poniszewska-Marańda

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Chand, R., Khan, S.U.R., Hussain, S., Wang, WL., Tang, MH., Ibrahim, N. (2024). Exploring Software Quality Through Data-Driven Approaches and Knowledge Graphs. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Poniszewska-Marańda, A. (eds) Good Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024. Lecture Notes in Networks and Systems, vol 990. Springer, Cham. https://doi.org/10.1007/978-3-031-60328-0_37

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