Quantitative Indicator for Objective Assessment of Software Process Quality and Performance
A company's software production and product quality can be improved by evaluating the software development process. Artefact inspection is one example of a flawed traditional method that relies on manual qualitative evaluations and is time-consuming, constrained by authority, and frequently su...
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| Format: | Article |
| Language: | English |
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Shaheed Zulfikar Ali Bhutto Institute of Science and Technology
2024-12-01
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| Series: | JISR on Computing |
| Subjects: | |
| Online Access: | http://jisrc.szabist.edu.pk/ojs/index.php/jisrc/article/view/170 |
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| _version_ | 1823858661946032128 |
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| author | Muhammad Adeel Mannan Jahangir Khan Hira Zafar |
| author_facet | Muhammad Adeel Mannan Jahangir Khan Hira Zafar |
| author_sort | Muhammad Adeel Mannan |
| collection | DOAJ |
| description |
A company's software production and product quality can be improved by evaluating the software development process. Artefact inspection is one example of a flawed traditional method that relies on manual qualitative evaluations and is time-consuming, constrained by authority, and frequently subjective. To address these limitations, this study introduces an innovative, semi-automatic method for assessing software processes, leveraging machine learning techniques. We define the problem as a sequence classification challenge that can be effectively tackled using machine learning algorithms. Building on this framework, we develop a new quantitative metric for the objective evaluation of software process efficiency and quality. To validate the effectiveness of our approach, we apply it to evaluate the defect management procedures employed in four real-world industrial software projects. Our empirical findings demonstrate that our method is effective and has the potential to deliver reliable, quantitative evaluations of software processes.
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| format | Article |
| id | doaj-art-ed3053f464964404bf3019c10f83a235 |
| institution | Kabale University |
| issn | 2412-0448 1998-4154 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Shaheed Zulfikar Ali Bhutto Institute of Science and Technology |
| record_format | Article |
| series | JISR on Computing |
| spelling | doaj-art-ed3053f464964404bf3019c10f83a2352025-02-11T10:35:24ZengShaheed Zulfikar Ali Bhutto Institute of Science and TechnologyJISR on Computing2412-04481998-41542024-12-0122210.31645/JISRC.24.22.2.4Quantitative Indicator for Objective Assessment of Software Process Quality and PerformanceMuhammad Adeel Mannan0Jahangir Khan1Hira Zafar2Faculty of Engineering Science and Technology, Hamdard University, Karachi, PakistanFaculty of Engineering Science and Technology, Hamdard University, Karachi, PakistanFaculty of Management Sciences, Khadim Ali Shah Bukhari (KASB) Institute of Technology Karachi, Pakistan A company's software production and product quality can be improved by evaluating the software development process. Artefact inspection is one example of a flawed traditional method that relies on manual qualitative evaluations and is time-consuming, constrained by authority, and frequently subjective. To address these limitations, this study introduces an innovative, semi-automatic method for assessing software processes, leveraging machine learning techniques. We define the problem as a sequence classification challenge that can be effectively tackled using machine learning algorithms. Building on this framework, we develop a new quantitative metric for the objective evaluation of software process efficiency and quality. To validate the effectiveness of our approach, we apply it to evaluate the defect management procedures employed in four real-world industrial software projects. Our empirical findings demonstrate that our method is effective and has the potential to deliver reliable, quantitative evaluations of software processes. http://jisrc.szabist.edu.pk/ojs/index.php/jisrc/article/view/170Machine LearningSoftware ProcessesQuantitative EvaluationSoftware Process Quality |
| spellingShingle | Muhammad Adeel Mannan Jahangir Khan Hira Zafar Quantitative Indicator for Objective Assessment of Software Process Quality and Performance JISR on Computing Machine Learning Software Processes Quantitative Evaluation Software Process Quality |
| title | Quantitative Indicator for Objective Assessment of Software Process Quality and Performance |
| title_full | Quantitative Indicator for Objective Assessment of Software Process Quality and Performance |
| title_fullStr | Quantitative Indicator for Objective Assessment of Software Process Quality and Performance |
| title_full_unstemmed | Quantitative Indicator for Objective Assessment of Software Process Quality and Performance |
| title_short | Quantitative Indicator for Objective Assessment of Software Process Quality and Performance |
| title_sort | quantitative indicator for objective assessment of software process quality and performance |
| topic | Machine Learning Software Processes Quantitative Evaluation Software Process Quality |
| url | http://jisrc.szabist.edu.pk/ojs/index.php/jisrc/article/view/170 |
| work_keys_str_mv | AT muhammadadeelmannan quantitativeindicatorforobjectiveassessmentofsoftwareprocessqualityandperformance AT jahangirkhan quantitativeindicatorforobjectiveassessmentofsoftwareprocessqualityandperformance AT hirazafar quantitativeindicatorforobjectiveassessmentofsoftwareprocessqualityandperformance |