摘要:错误定位方法大多通过分析语句覆盖信息来标识出导致程序失效的可疑语句.其中，语句覆盖信息通常以语句执行或语句未执行的二进制状态信息来表示.然而，该二进制状态信息仅表明该语句是否被执行的信息，无法体现该语句在具体执行中的重要程度，可能会降低错误定位的有效性.为了解决这个问题，提出了基于词频-逆文件频率的错误定位方法.该方法采用词频-逆文件频率技术识别出单个测试用例中语句的影响程度高低，从而构建出具有语句重要程度识别度的信息模型，并基于该模型来计算语句的可疑值.实验结果表明，该方法大幅提升了错误定位的效能.



Abstract:Most existing fault localization approaches utilize statement coverage information to identify suspicious statements potentially responsible for failures. They generally use the binary status information to represent the statement coverage information, indicating a statement executed or not executed. However, the binary information just shows whether a statement is executed or not whereas it cannot evaluate the importance of a statement in a specific execution. Consequently, this may degrade fault localization performance. To address this issue, this study proposes a fault localization approach using term frequency and inverse document frequency. Specifically, the proposed approach constructs an information model to successfully identify the influence of a statement in a test case, and uses the information model to evaluate the suspiciousness of a statement of being faulty. The experiments show that the proposed approach significantly improves fault localization effectiveness.



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