软件产品线的多目标测试生成

Christopher Henard, Mike Papadakis, Gilles Perrouin, Jacques Klein, Yves Le Traon
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引用次数: 108

摘要

软件产品线(SPLs)是一组共享代表软件产品代码或功能的公共资产的产品。这些资产被表示为特征,通常被组织到特征模型中,用户可以从特征模型中配置软件产品。一般来说,很少有特性足以配置数百万个软件产品。因此,选择符合给定测试目标的产品是一个难题。测试过程通常涉及多个潜在的相互冲突的测试目标,例如,最大化可选功能的数量,同时最小化产品的数量和最小化测试成本。然而,大多数生成产品的方法通常只针对一个目标,比如测试最大数量的功能交互。虽然在某些情况下专注于一个目标可能是足够的,但这种做法并不能反映真实的测试情况。本文提出了一种遗传算法,用于处理SPLs测试生成中的多个冲突目标。在不同尺寸的FMs上进行的实验验证了该方法的有效性、可行性和实用性。
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Multi-objective test generation for software product lines
Software Products Lines (SPLs) are families of products sharing common assets representing code or functionalities of a software product. These assets are represented as features, usually organized into Feature Models (FMs) from which the user can configure software products. Generally, few features are sufficient to allow configuring millions of software products. As a result, selecting the products matching given testing objectives is a difficult problem. The testing process usually involves multiple and potentially conflicting testing objectives to fulfill, e.g. maximizing the number of optional features to test while at the same time both minimizing the number of products and minimizing the cost of testing them. However, most approaches for generating products usually target a single objective, like testing the maximum amount of feature interactions. While focusing on one objective may be sufficient in certain cases, this practice does not reflect real-life testing situations. The present paper proposes a genetic algorithm to handle multiple conflicting objectives in test generation for SPLs. Experiments conducted on FMs of different sizes demonstrate the effectiveness, feasibility and practicality of the introduced approach.
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