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Multiple Fault Localization Using Constraint Programming and Pattern Mining

机译:使用约束编程和模式挖掘的多故障定位

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Fault localization problem is one of the most difficult processes in software debugging. The current constraint-based approaches draw strength from declarative data mining and allow to consider the dependencies between statements with the notion of patterns. Tackling large faulty programs is clearly a challenging issue for Constraint Programming (CP) approaches. Programs with multiple faults raise numerous issues due to complex dependencies between faults, making the localization quite complex for all of the current localization approaches. In this paper, we provide a new CP model with a global constraint to speed-up the resolution and we improve the localization to be able to tackle multiple faults. Finally, we give an experimental evaluation that shows that our approach improves on CP and standard approaches.
机译:故障定位问题是软件调试中最困难的过程之一。当前基于约束的方法从声明式数据挖掘中汲取了力量,并允许考虑具有模式概念的语句之间的依赖关系。解决大型错误程序显然是约束编程(CP)方法的一个挑战性问题。带有多个故障的程序由于故障之间的复杂依赖性而引发了许多问题,这使得对于所有当前的本地化方法而言,本地化都相当复杂。在本文中,我们提供了具有全局约束的新CP模型以加快分辨率,并改善了定位能力以解决多个故障。最后,我们进行了实验评估,结果表明我们的方法在CP和标准方法上有所改进。

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