Maximizing Refactoring Coverage in an Automated Maintenance Approach Using Multi-Objective Optimization

2019 IEEE/ACM 3rd International Workshop on Refactoring (IWoR) Pub Date : 2019-05-01 DOI:10.1109/IWoR.2019.00014

M. Mohan, D. Greer, P. McMullan

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Abstract

This paper describes a multi-objective genetic algorithm used to automate software refactoring. The approach is validated using a set of open source Java programs with a purpose built tool, MultiRefactor. The tool uses a metric function to measure quality in a software system and tests a second objective to measure the amount of code coverage of the applied refactorings by analyzing the code elements they have been applied to. The multi-objective setup will refactor the input program to improve its quality using the quality objective, while also maximizing the code coverage of the refactorings applied to the software. An experiment has been constructed to measure the multi-objective approach against the alternative mono-objective approach that does not use an objective to measure refactoring coverage. The two approaches are tested on six different open source Java programs. The multi-objective approach is found to give significantly better refactoring coverage scores across all inputs in a similar time, while also generating improvements in the quality scores.

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在使用多目标优化的自动化维护方法中最大化重构覆盖率

本文描述了一种用于软件重构自动化的多目标遗传算法。使用一组开源Java程序和专门构建的工具MultiRefactor对该方法进行了验证。该工具使用一个度量函数来度量软件系统的质量，并测试第二个目标，通过分析应用重构的代码元素来度量应用重构的代码覆盖率。多目标设置将重构输入程序，以使用质量目标来提高其质量，同时也最大化应用于软件的重构的代码覆盖率。已经构建了一个实验来度量多目标方法与不使用目标来度量重构覆盖率的可选单目标方法。这两种方法在六个不同的开源Java程序上进行了测试。我们发现，在相同的时间内，多目标方法在所有输入中给出了更好的重构覆盖率分数，同时也提高了质量分数。

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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来源期刊

2019 IEEE/ACM 3rd International Workshop on Refactoring (IWoR)

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