Cognitive Complexity Applied to Software Development: An Automated Procedure to Reduce the Comprehension Effort

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of ICT Research and Applications Pub Date : 2022-12-31 DOI:10.5614/itbj.ict.res.appl.2022.16.3.6
D. Wijendra, K. Hewagamage
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引用次数: 0

Abstract

The cognitive complexity of a software application determines the amount of human effort required to comprehend its internal logic, which results in a subjective measurement. The quantification process of the cognitive complexity as a metric is problematic since the factors representing the computation do not represent the exact human cognition. Therefore, the determination of cognitive complexity requires expansion beyond its quantification. The human comprehension effort related with a software application is associated with each phase of its development process. Correct requirements identification and accurate logical diagram generation prior to code implementation can lead to proper logical identification of software applications. Moreover, human comprehension is essential for software maintenance. Defect identification, correction and handling of code quality issues cannot be maintained without good comprehension. Therefore, cognitive complexity can be effectively applied to demonstrate human understandability inside the respective phases of requirements analysis, design, defect tracking, and code quality optimization. This study involved automation of the above-mentioned phases to reduce the manual human cognitive load and reduce cognitive complexity. It was found that the proposed system could enhance the average accuracy of requirements analysis and class diagram generation by 14.44% and 9.89% average accuracy incrementation through defect tracking and code quality issues compared to manual procedures.
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认知复杂性应用于软件开发:减少理解工作的自动化过程
软件应用程序的认知复杂性决定了理解其内部逻辑所需的人力工作量,这导致了主观测量。认知复杂性作为一种度量的量化过程是有问题的,因为代表计算的因素并不代表人类的确切认知。因此,认知复杂性的确定需要在量化之外进行扩展。与软件应用程序相关的人类理解工作与其开发过程的每个阶段都相关。在代码实现之前,正确的需求识别和准确的逻辑图生成可以导致软件应用程序的正确逻辑识别。此外,人的理解对于软件维护是必不可少的。如果没有良好的理解,就无法维护缺陷识别、纠正和代码质量问题的处理。因此,认知复杂性可以有效地应用于证明人类在需求分析、设计、缺陷跟踪和代码质量优化各个阶段的可理解性。本研究涉及上述阶段的自动化,以减少人工人类认知负荷并降低认知复杂性。研究发现,与手动程序相比,通过缺陷跟踪和代码质量问题,所提出的系统可以将需求分析和类图生成的平均精度提高14.44%和9.89%。
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来源期刊
Journal of ICT Research and Applications
Journal of ICT Research and Applications COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
1.60
自引率
0.00%
发文量
13
审稿时长
24 weeks
期刊介绍: Journal of ICT Research and Applications welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless & Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management. Authors are invited to submit articles that have not been published previously and are not under consideration elsewhere.
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