Software systems are often released without formal specifications. To deal with the problem of lack of and outdated specifications, rule-based specification mining approaches have been proposed. These approaches analyze execution traces of a system to infer the rules that characterize the protocols, typically of a library, that its clients must obey. Rule-based specification mining approaches work by exploring the search space of all possible rules and use interestingness measures to differentiate specifications from false positives. Previous rule-based specification mining approaches often rely on one or two interestingness measures, while the potential benefit of combining multiple available interestingness measures is not yet investigated. In this work, we propose a learning to rank based approach that automatically learns a good combination of 38 interestingness measures. Our experiments show that the learning to rank based approach outperforms the best performing approach leveraging single interestingness measure by up to 66%.

中文翻译：

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利用学习排序的基于规则的规范挖掘

软件系统通常在没有正式规范的情况下发布。为了解决规范缺乏和过时的问题，已经提出了基于规则的规范挖掘方法。这些方法分析系统的执行轨迹，以推断出其客户端必须遵守的协议（通常是图书馆的协议）的特征规则。基于规则的规范挖掘方法通过探索所有可能规则的搜索空间并使用兴趣度度量来区分规范和误报。以前基于规则的规范挖掘方法通常依赖于一两个兴趣度度量，而尚未研究组合多个可用兴趣度度量的潜在好处。在这项工作中，我们提出了一种基于学习排序的方法，该方法可以自动学习 38 个有趣度度量的良好组合。我们的实验表明，基于学习排名的方法比利用单一兴趣度度量的最佳表现方法高出 66%。