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，通常是压倒性的。因此，开发人员经常花费大量的时间来确定最佳选择。这个问题与越来越多的软件开发支持工具/平台提供的大量信息有关。为了过滤信息，提高软件开发效率，软件工程推荐系统为信息项（代码、专家等）提供建议。开发商最感兴趣的项目已经出现。然而，尽管现有的SE推荐系统的实验性能不断提高，最近的调查显示，开发人员仍然犹豫不决，采用数据驱动的推荐系统，由于其不稳定的性能在实践中，无法解释所提供的建议。拟议的研究计划将创建可靠和可解释的SE推荐系统，使开发人员能够信任和充分利用新一代人工智能（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环境中始终如一地执行。但是，开发人员无法实现适用于每个特定使用环境的专用版本的编译器。因此，上下文感知和自适应是必不可少的，以实现可靠的SE推荐系统。现有的上下文感知SE建模器远不是理想的，因为它们忽略了上下文的抽象并且无法相应地适应。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，该适配器可以利用用户的隐式和显式反馈来处理范围广泛的更改。为确保可靠的SE开发人员，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推荐系统被视为黑箱，因为他们不清楚的工作机制，导致不信任的系统，并需要使用一个耗时的试错过程来部署一个高性能的推荐系统。为了解决这一挑战并构建可解释的解释器，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，并将设计可以自动提供预期解释的机器学习模型。 根据该计划，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。该计划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行业受益，从而增强加拿大在构建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.