A Process for Fault-Driven Repair of Constraints Among Features

Paolo Arcaini, A. Gargantini, M. Radavelli
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Abstract

The variability of a Software Product Line is usually both described in the problem space (by using a variability model) and in the solution space (i.e., the system implementation). If the two spaces are not aligned, wrong decisions can be done regarding the system configuration. In this work, we consider the case in which the variability model is not aligned with the solution space, and we propose an approach to automatically repair (possibly) faulty constraints in variability models. The approach takes as input a variability model and a set of combinations of features that trigger conformance faults between the model and the real system, and produces the repaired set of constraints as output. The approach consists of three major phases. First, it generates a test suite and identifies the condition triggering the faults. Then, it modifies the constraints of the variability model according to the type of faults. Lastly, it uses a logic minimization method to simplify the modified constraints. We evaluate the process on variability models of 7 applications of various sizes. An empirical analysis on these models shows that our approach can effectively repair constraints among features in an automated way.
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特征间约束的故障驱动修复过程
软件产品线的可变性通常是在问题空间(通过使用可变性模型)和解决方案空间(即系统实现)中描述的。如果这两个空格没有对齐,就会对系统配置做出错误的决策。在这项工作中,我们考虑了可变性模型与解空间不一致的情况,并且我们提出了一种在可变性模型中自动修复(可能)错误约束的方法。该方法将可变性模型和一组触发模型与实际系统之间一致性错误的特征组合作为输入,并产生一组修复的约束作为输出。该方法包括三个主要阶段。首先,它生成一个测试套件,并识别触发故障的条件。然后,根据故障类型对变异模型的约束条件进行修正。最后,采用逻辑最小化方法对修改后的约束进行简化。我们对7个不同规模的应用程序的变异性模型进行了评估。对这些模型的实证分析表明,我们的方法可以有效地以自动化的方式修复特征之间的约束。
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