Context

Software Fault Localisation (FL) refers to finding faulty software elements related to failures produced as a result of test case execution. This is a laborious and time consuming task. To allow FL automation search-based algorithms have been successfully applied in the field of Search-Based Fault Localisation (SBFL). However, there is no study mapping the SBFL field to the best of our knowledge and we believe that such a map is important to promote new advances in this field.

Objective

To present the results of a mapping study on SBFL, by characterising the proposed methods, identifying sources of used information, adopted evaluation functions, applied algorithms and elements regarding reported experiments.

Method

Our mapping followed a defined process and a search protocol. The conducted analysis considers different dimensions and categories related to the main characteristics of SBFL methods.

Results

All methods are grounded on the coverage spectra category. Overall the methods search for solutions related to suspiciousness formulae to identify possible faulty code elements. Most studies use evolutionary algorithms, mainly Genetic Programming, by using a single-objective function. There is little investigation of real-and-multiple-fault scenarios, and the subjects are mostly written in C and Java. No consensus was observed on how to apply the evaluation metrics.

Conclusions

Search-based fault localisation has seen a rise in interest in the past few years and the number of studies has been growing. We identified some research opportunities such as exploring new sources of fault data, exploring multi-objective algorithms, analysing benchmarks according to some classes of faults, as well as, the use of a unique definition for evaluation measures.

中文翻译：

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基于搜索的故障定位：系统的映射研究

语境

软件故障本地化（FL）指查找与由于测试用例执行而产生的故障有关的故障软件元素。这是一项费时费力的任务。为了允许FL自动化，基于搜索的算法已成功应用于基于搜索的故障定位（SBFL）领域。但是，目前尚无研究将SBFL领域映射到我们所掌握的全部知识，并且我们认为这样的映射对于促进该领域的新进展很重要。

目的

通过表征所提出的方法，确定所用信息的来源，所采用的评估功能，所应用的算法和有关已报道实验的要素，来介绍SBFL的映射研究结果。

方法

我们的映射遵循定义的过程和搜索协议。进行的分析考虑了与SBFL方法的主要特征相关的不同维度和类别。

结果

所有方法都基于覆盖光谱类别。总体而言，这些方法搜索与可疑公式有关的解决方案，以识别可能的错误代码元素。大多数研究通过使用单目标函数来使用进化算法，主要是遗传编程。很少有关于实际和多重故障场景的调查，并且这些主题大多是用C和Java编写的。在如何应用评估指标方面未达成共识。

结论

在过去的几年中，基于搜索的故障定位已经引起人们的兴趣，并且研究数量也在不断增长。我们发现了一些研究机会，例如探索故障数据的新来源，探索多目标算法，根据某些故障类别分析基准，以及使用唯一的定义进行评估。