Reliable and Explainable Recommender Systems for Efficient Software Development

现代软件开发是复杂的，开发人员面临的选择数量，例如实现功能的方法，往往是压倒性的。因此，开发人员经常花费大量时间来确定最佳选择。随着越来越多的软件开发支持工具/平台所提供的大量信息，这个问题变得更加严重。为了过滤信息并提高软件开发的效率，出现了软件工程（SE）推荐系统，它为开发人员最有可能感兴趣的信息项（代码、专家等）提供建议。然而，尽管现有SE推荐系统的实验性能不断提高，但最近的调查显示，由于数据驱动的推荐系统在实践中的性能不稳定，并且无法解释所提供的推荐，开发人员仍然对采用数据驱动的推荐系统犹豫不决。拟议的研究计划将创建可靠且可解释的SE推荐系统，使开发人员能够信任并充分利用下一代人工智能（AI）授权的软件开发工具。一个可靠的SE推荐器应该在不断变化的推荐上下文中始终如一地执行。然而，对于开发人员来说，实现适合每个特定使用环境的推荐程序的专用版本是不可伸缩的。因此，上下文感知和自适应对于实现可靠的SE推荐系统至关重要。现有的上下文感知SE推荐器远不是理想的，因为它们忽略了上下文的抽象，并且无法相应地进行调整。为了填补这一空白，我们将为每个目标推荐任务设计一个上下文解释组件，并在推荐模型中设计一个适配器，该适配器可以利用来自用户的隐式和显式反馈来处理大范围的更改。为了确保可靠的SE推荐，我们还将开发新的方法来提高软件数据质量。大多数现有的SE推荐系统被视为黑盒，因为它们的工作机制不明确，导致对系统的不信任，并且需要使用耗时的试错过程来部署高性能的推荐系统。为了解决这一挑战并构建可解释的推荐器，我们将通过分析开发人员的在线行为和调查从业人员来确定SE推荐任务的预期解释形式，并将设计能够自动提供预期解释的机器学习模型。在该项目下，3名博士、3名硕士和2名本科生将接受管理大型软件数据集和构建智能工具的培训，以促进高效的软件开发。该项目将通过为软件开发提供有效、可靠和可解释的自动化解决方案，使快速增长的信息技术产业受益，从而增强加拿大在建立人工智能软件开发环境方面的领导地位。

Modern software development is complex, and the number of choices developers face, such as ways to implement a feature, is often overwhelming. Developers thus often spend an enormous amount of time determining the optimal choice. This problem worsens with the flood of information provided by an increasing number of software development support tools/platforms. To filter information and improve the efficiency of software development, software engineering (SE) recommender systems, which provide suggestions for information items (code, experts, etc.) that are most likely of interest to developers, have emerged. However, despite the increasing experimental performance of existing SE recommender systems, recent surveys reveal that developers are still hesitant to adopt data-driven recommender systems due to their unstable performance in practice and inability to explain the provided recommendations. The proposed research program will create reliable and explainable SE recommender systems to enable developers to trust and fully utilize the coming generation of artificial intelligence (AI) empowered software development tools.   A reliable SE recommender should perform consistently given an evolving recommendation context. However, it is not scalable for developers to implement dedicated versions of recommenders that are suitable for each specific context of use. As such, being context-aware and adaptive are essential to achieving reliable SE recommender systems. Existing context-aware SE recommenders are far from ideal as they ignore the abstraction of the context and are unable to adapt accordingly. To fill this gap, we will design a context interpretation component for each target recommendation task and an adapter in the recommendation model that can handle a broad scope of changes leveraging implicit and explicit feedback from users. To ensure reliable SE recommenders, we will also develop new methodologies to improve software data quality.   Most existing SE recommender systems are treated as black boxes because of their unclear working mechanisms, resulting in mistrust of the systems and the need to use a time-consuming trial-and-error process to deploy a high-performance recommender system. To solve this challenge and build explainable recommenders, we will identify expected explanation forms for SE recommendation tasks by analyzing developers' online behaviours and surveying practitioners and will design machine learning models that can provide the expected explanations automatically.   Under this program, 3 PhD, 3 MSc and 2 undergraduate students will be trained in managing large software datasets and building intelligence tools to facilitate efficient software development. The program will benefit the rapidly-growing information technology industry by providing effective, reliable and explainable automation solutions for software development, thereby enhancing Canada's leadership in building an AI-empowered software development environment.