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Technical skills are widely considered a benchmark for measuring the code comprehension proficiency of programmers, overlooking their non-technical skills. When the programmers deal with various kinds of code snippets issues, they are obliged to perceive the issues, which requires them to utilize their non-technical skills such as problem-solving and decision-making skills. These skills act as a foundation for exhibiting their technical skills by utilizing their programming knowledge (belief), fulfilling their desired goals by taking relevant intended actions by staying committed throughout the maintenance phase. Therefore, the aim of our work is to study the impact of belief, desire, intention, and commitment alongside technical skills on the code comprehension skills of programmers. The empirical work conducted in this study measures the proficiency level of 158 participants in the form of a comprehension proficiency index. The dataset collected from these participants consists of technical and non-technical data. The technical data is used to measure the technical index whereas, non-technical data is used to measure the belief, desire, and commitment indices of programmers, thus acting as inputs to measure their actual comprehension capabilities. The observed comprehension proficiency index values through various case study scenarios corroborate that, integrating technical and non-technical skills for the measurement of code comprehension skills of programmers yields more true-to-life results in comparison to merely observing technical skills. These case studies also highlight the technical and non-technical strengths and weaknesses of participants. The results conclude that by considering BDIC parameters along with technical parameters, 54 participants experienced a change in their expertise class based on the assessed comprehension proficiency index values, thus, impacting their overall code comprehension skills. Furthermore, these obtained comprehension proficiency index values are used to rank the participants based on their performance. From an industrial point of view, the results obtained through our proposed methodology can help managers assign maintenance tasks based on the ranking generated through the achieved skill level of programmers.

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An Empirical Analysis on Investigating the Impact of Belief, Desire, Intention, and Commitment on Programmers’ Code Comprehension Proficiency

  • 会议论文

  • 发表日期：

    2026年

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  • Divjot Singh,
  • Ashutosh Mishra,
  • Ashutosh Aggarwal

摘要

Technical skills are widely considered a benchmark for measuring the code comprehension proficiency of programmers, overlooking their non-technical skills. When the programmers deal with various kinds of code snippets issues, they are obliged to perceive the issues, which requires them to utilize their non-technical skills such as problem-solving and decision-making skills. These skills act as a foundation for exhibiting their technical skills by utilizing their programming knowledge (belief), fulfilling their desired goals by taking relevant intended actions by staying committed throughout the maintenance phase. Therefore, the aim of our work is to study the impact of belief, desire, intention, and commitment alongside technical skills on the code comprehension skills of programmers. The empirical work conducted in this study measures the proficiency level of 158 participants in the form of a comprehension proficiency index. The dataset collected from these participants consists of technical and non-technical data. The technical data is used to measure the technical index whereas, non-technical data is used to measure the belief, desire, and commitment indices of programmers, thus acting as inputs to measure their actual comprehension capabilities. The observed comprehension proficiency index values through various case study scenarios corroborate that, integrating technical and non-technical skills for the measurement of code comprehension skills of programmers yields more true-to-life results in comparison to merely observing technical skills. These case studies also highlight the technical and non-technical strengths and weaknesses of participants. The results conclude that by considering BDIC parameters along with technical parameters, 54 participants experienced a change in their expertise class based on the assessed comprehension proficiency index values, thus, impacting their overall code comprehension skills. Furthermore, these obtained comprehension proficiency index values are used to rank the participants based on their performance. From an industrial point of view, the results obtained through our proposed methodology can help managers assign maintenance tasks based on the ranking generated through the achieved skill level of programmers.