Automatic Identification of Code Changes That Require Extensive Testing

要做的工作的性质和重要性：执行软件质量保证（SQA）活动是为了确保对软件系统的更改不会在软件系统属性（例如，功能，性能）中引入回归。SQA是一种资源受限的活动——对软件组织产生的所有更改进行详尽的验证实际上是不可行的。SQA**资源应该从低风险的更改重定向到高风险的更改，以尽量减少软件系统属性（例如，bug）因影响用户体验而出现回归的可能性。在Shopify，额外的SQA资源被分配给开发团队认为有风险的更改。然而，这些决策可能很容易出错，因为“直觉”常常涉及其中，从而导致SQA资源的次优分配。在这个项目中，我们将利用Shopify的历史开发数据来支持SQA资源分配决策。**预期结果：简而言之，我们将训练预测模型，以估计**每个代码更改的风险评分。这些预测模型将集成到Shopify代码更改过程中。**与公司的相关性：这一历史视角有可能改变**Shopify执行SQA的方式。这些集成的风险评分将帮助开发人员对应该分配额外SQA资源的更改做出基于数据的决策**。**加拿大将如何受益：该项目将为两名硕士研究生（Farida El Zanaty夫人和Christophe Rezk先生）提供大规模统计回归和应用**机器学习的培训机会，以解决现实世界的工业问题。这些宝贵的技能将有助于满足加拿大公共和私营部门对数据科学家日益增长的需求。

NATURE AND IMPORTANCE OF THE WORK TO BE DONE: Software Quality Assurance (SQA)**activities are performed to ensure that changes to software systems do not introduce regression in software**system properties (e.g., functionality, performance). SQA is a resource-constrained activity-exhaustive**verification of all of the changes that a software organization produces is not practically feasible. SQA**resources should be redirected from low-risk changes to high-risk ones in order to minimize the likelihood of**regressions in software system properties (e.g., bugs) from impacting user experience. At Shopify, additional**SQA resources are allocated to changes that the development team deems to be risky. Yet these decisions may**be error-prone, since "gut feel" is often involved, resulting in a suboptimal allocation of SQA resources. In this**project, we will leverage Shopify's historical development data to support SQA resource allocation decisions.**ANTICIPATED OUTCOMES: In a nutshell, we will train predictive models that will estimate a risk score for**each code change. These predictive models will be integrated into the Shopify code change process.**RELEVANCE TO THE COMPANY: This historical perspective has the potential to transform the way that**Shopify performs SQA. These integrated risk scores will help developers to make data-informed decisions**about the changes to which additional SQA resources should be allocated.**HOW CANADA WILL BENEFIT: This project will provide an opportunity for two master's students (Mrs.**Farida El Zanaty and Mr. Christophe Rezk) to be trained in large-scale statistical regression and applied**machine learning to solve real-world industrial problems. These are invaluable skills that will help to fill the**growing demand for data scientists in the Canadian public and private sectors.