A METRICS -BASED MODEL FOR ESTIMATING THE MAINTENANCE EFFORT OF PYTHON SOFTWARE

International Journal of Software Engineering & Applications Pub Date : 2023-05-26 DOI:10.5121/ijsea.2023.14302

C. Mukunga, John Gichuki Ndia, G. Wambugu

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Abstract

Software project management includes a substantial area for estimating software maintenance effort. Estimation of software maintenance effort improves the overall performance and efficiency of software. The Constructive Cost Model (COCOMO) and other effort estimation models are mentioned in literature but are inappropriate for Python programming language. This research aimed to modify the Constructive Cost Model (COCOMO II) by considering a range of Python maintenance effort influencing factors to get estimations and incorporated size and complexity metrics to estimate maintenance effort. A within-subjects experimental design was adopted and an experiment questionnaire was administered to forty subjects aiming to rate the maintainability of twenty Python programs. Data collected from the experiment questionnaire was analyzed using descriptive statistics. Metric values were collected using a developed metric tool. The subject ratings on software maintainability were correlated with the developed model’s maintenance effort, a strong correlation of 0.610 was reported meaning that the model is valid.

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一个基于度量的模型，用于估计python软件的维护工作量

软件项目管理包括评估软件维护工作的重要领域。对软件维护工作的评估可以提高软件的整体性能和效率。构建成本模型(COCOMO)和其他工作量估计模型在文献中被提及，但不适合Python编程语言。本研究旨在修改构建成本模型(COCOMO II)，通过考虑一系列Python维护工作量的影响因素来获得估算，并纳入规模和复杂性指标来估算维护工作量。采用受试者内实验设计，对40名受试者进行问卷调查，对20个Python程序的可维护性进行评分。从实验问卷中收集的数据采用描述性统计进行分析。使用开发的度量工具收集度量值。受试者对软件可维护性的评分与开发的模型的维护工作相关，相关性为0.610，这意味着该模型是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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International Journal of Software Engineering & Applications

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