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An Adaptive Search Budget Allocation Approach for Search-Based Test Case Generation

机译：基于搜索的测试用例生成的自适应搜索预算分配方法

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Search-based techniques have been successfully used to automate test case generation. Such approaches allocate a fixed search budget to generate test cases aiming at maximizing code coverage. The search budget plays a crucial role; due to the hugeness of the search space, the higher the assigned budget, the higher the expected coverage. Code components have different structural properties that may affect the ability of search-based techniques to achieve a high coverage level. Thus, allocating a fixed search budget for all the components is not recommended and a component-specific search budget should be preferred. However, deciding the budget to assign to a given component is not a trivial task.In this article, we introduce Budget Optimization for Testing (BOT), an approach to adaptively allocate the search budget to the classes under test. BOT requires information about the branch coverage that will be achieved on each class with a given search budget. Therefore, we also introduce BRANCHOS, an approach that predicts coverage in a budget-aware way. The results of our experiments show that (ⅰ) BRANCHOS can approximate the branch coverage in time with a low error, and (ⅱ) BOT can significantly increase the coverage achieved by a test generation tool and the effectiveness of generated tests.
机译：基于搜索的技术已成功用于自动化测试用例。此类方法分配固定的搜索预算以生成旨在最大化代码覆盖率的测试用例。搜索预算起到至关重要的作用;由于搜索空间的拥抱，所分配的预算越高，预期覆盖率越高。代码组件具有不同的结构属性，可能会影响基于搜索的技术实现高覆盖率的能力。因此，不建议为所有组件分配固定的搜索预算，并且应该优选特定于组件的搜索预算。但是，将预算分配给给定的组件不是一个微不足道的任务。在本文中，我们介绍了测试（Bot）的预算优化，一种自适应地将搜索预算分配给被测类的方法。机器人需要有关分支覆盖范围的信息，这些分支覆盖将在具有给定的搜索预算的每个类上实现。因此，我们还介绍了Branchos，一种预测预算感知方式的覆盖的方法。我们的实验结果表明，（Ⅰ）Branchos可以用低误差估计分支覆盖率，并且（Ⅱ）机器人可以显着增加测试生成工具和产生的测试的有效性所实现的覆盖率。

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