Software testing (ST) has been considered as one of the most important and critical activities of the software development life cycle (SDLC) since it influences directly on quality. When a software project is planned, it is common practice to predict the corresponding ST effort (STEP) as a percentage of predicted SDLC effort. However, the effort range for ST has been reported between 10 and 60% of the predicted SDLC effort. This wide range on STEP causes uncertainty in software managers due to STEP is used for allocating resources to teams exclusively for testing activities, and for budgeting and bidding the projects. In spite of this concern, hundreds of studies have been published since 1981 about SDLC effort prediction models, and only thirty-one STEP studies published in the last two decades were identified (just two of them based their conclusions on statistical significance). The contribution of the present study is to investigate the application for STEP of five machine learning (ML) models reported as the most accurate ones when applied to SDLC effort prediction. The models were trained and tested with data sets of projects selected from an international public repository of software projects. The selection for projects was based on their data quality rating, type of development, development platform, programming language generation, sizing method, and resource level of projects. Results based on statistical significance allow suggesting the application of specific ML models to software projects by type of development, and developed on a determined platform and programming language generation.

软件测试（ST）被认为是软件开发生命周期（SDLC）的最重要和最关键的活动之一，因为它直接影响质量。计划软件项目时，通常的做法是将相应的ST工作量（STEP）预测为SDLC工作量的百分比。但是，据报道，ST的努力范围是SDLC预测努力的10％至60％。由于STEP用于将资源专门分配给团队用于测试活动以及预算和投标项目，因此STEP上的这种广泛范围导致软件管理器中存在不确定性。尽管存在这种担忧，但自1981年以来，已经发表了数百篇有关SDLC努力预测模型的研究，在过去的二十年中，只有31篇关于STEP的研究被确定（其中只有两项基于统计学意义得出的结论）。本研究的目的是调查五个机器学习（ML）模型在STEP上的应用，这些模型被报告为最准确的模型，当它们应用于SDLC努力预测时。使用从国际软件项目公共知识库中选择的项目数据集对模型进行了训练和测试。根据项目的数据质量等级，开发类型，开发平台，编程语言生成，大小确定方法和项目资源水平来选择项目。基于统计显着性的结果可以建议按开发类型将特定ML模型应用于软件项目，