Intelligent Detection of Open Source Software Anomalies

没有经过充分测试的软件，在执行过程中可能会出现意想不到的结果，这种结果被称为软件异常（SA）。特定的SA称为隐含场景（Implied Scenario, is）：在执行期间隐含一个新的场景。IS的一个流行例子是锅炉系统，其中控制组件根据以前从传感器收集的数据而不是当前数据向执行器发送命令。IS的早期修复降低了成本。因此，信息系统研究依赖于从序列图（SD）中研究信息系统。大多数研究都需要人类的专业知识（例如注释SD），这很容易出错，也很耗时。这些方法不能帮助开发人员在开发软件时避免IS。而且，在实践中，公司并不使用或记录SD。另一方面，有很多研究构建SA预测算法（使用异常报告数据和添加的代码行等度量），以确定软件中更有可能发生SA的部分。然而，这些作品并不直接针对IS。目标。该研究计划的短期目标是开发基于启发式的技术和工具，用于使用SA预测指标进行is预测，这将帮助软件开发人员在开发软件时自动分析is代码并将结果可视化。开源软件（OSS）是本研究的主要焦点，因为越来越多的公司正在采用OSS解决方案，同时需要支持公司管理和集成OSS。方法。在程序的第一个流中，我们将研究可用于IS的SA预测指标，并开发IS预测指标数据库。我们将进行系统的文献综述，并探索公共SA报告数据集，以建立一个IS库。我们还将研究SA预测研究，并使用主题建模为IS图书馆开发IS预测指标数据库。其次，我们将基于启发式方法建立IS预测模型。我们将探索不同的算法和指标来提高IS的预测精度。此外，我们将研究机器学习方法的自动化，以开发自动选择预测指标的模型。最后，我们将学习可视化技术和OSS设计模式，以构建用于分析IS代码的开源工具。我们将使用我们的原型用各种数据集评估我们的模型，并将结果与当前的IS预测工作进行比较。该工具的有用性将在全球范围内对开发人员进行在线调查。HQP和影响。我期待在IS预测研究方面的实践和理论贡献，这将有利于加拿大软件行业生产高质量的软件（即避免IS）和公司集成OSS解决方案。该项目将培养具有数据科学和软件工程专业知识的高素质人才（HQP）。HQP将以第一作者身份在顶级同行评议场所发表论文。

Software that is not fully tested, can show unexpected results during execution that is referred as Software Anomaly (SA). Specific SA is known as Implied Scenario (IS): a new scenario is implied during execution time. A popular example of IS is a boiler system in which the control component sends commands to an actuator based on the previous collected data from sensors instead of the current data. The earlier fixing of IS reduces the costs. Hence, IS research rely on studying IS from Sequence Diagrams (SD). Most research require human expertise (e.g. to annotate SD), which is error-prone and time consuming. These approaches cannot help developers to avoid IS while developing a software. Also, in practice, SD is not used or documented by companies. On the other hand, there is much research that build SA prediction algorithms (using anomaly reports data with metrics such as lines of code added) to determine parts of software that are more probable to have SA. However, these works do not directly address IS. Objective. The short-term objective of this research program is to develop heuristic-based techniques and tools for IS prediction using the SA prediction metrics, which will help software developers to automatically analyze the code for IS and visualize the results while developing software. Open Source Software (OSS) is the main focus of this research due to the increasing number of companies that are adopting OSS solutions, while there is a need to support companies in managing and integrating the OSS. Methods. In the first stream of the program we will investigate the SA prediction metrics that can be used for IS and develop an IS prediction metrics database. We will conduct systematic literature review and explore public SA report datasets to develop an IS library. We will also study SA prediction research and use topic modelling to develop an IS Prediction Metrics Database for the IS library. Secondly, we will build IS prediction models based on heuristic approaches. We will explore different algorithms and metrics to increase IS prediction accuracy. Also, we will study automation of machine learning approaches to develop models that choose prediction metrics automatically. Finally, we will study visualization techniques and OSS design patterns to build open source tools for analyzing code for IS. We will evaluate our models with various datasets using our prototypes and compare results with the current IS prediction works. The usefulness of the tool will be surveyed online for developers worldwide. HQP and Impact. I expect practical and theoretical contributions in IS prediction research that will benefit Canadian software industry in producing high quality software (i.e. avoiding IS) and companies in integrating OSS solutions. The program will train Highly Qualified Personnel (HQP) with expertise in data science and software engineering. The HQP will publish in top-ranked peer-reviewed venues as the first author.