Automatic Identification of Code Changes That Require Extensive Testing

自动识别需要大量测试的代码更改

• 批准号: 531224-2018
• 负责人: McIntosh, Shane
• 金额: $ 1.82万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=659303
• 关键词: Automatic; Identification; Code; Changes; Require

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.

要完成的工作的性质和重要性：执行软件质量保证(SQA)**活动，以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。应将SQA**资源从低风险更改重定向到高风险更改，以最大限度地减少**影响用户体验的软件系统属性(例如错误)倒退的可能性。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**容易出错，因为经常涉及“直觉”，导致SQA资源的次优分配。在这个**项目中，我们将利用Shopify的历史发展数据来支持SQA资源分配决策。**预期结果：简而言之，我们将训练预测模型，该模型将估计**每次代码更改的风险分数。这些预测模型将被集成到Shopify代码更改流程中。**与公司的相关性：这种历史视角有可能改变**Shopify执行SQA的方式。这些综合风险分数将帮助开发人员做出**关于应向其分配额外SQA资源的更改的数据知情决策。**加拿大将如何受益：该项目将为两名硕士学生(**Farida El Zanati夫人和Christophe Rezk先生)提供一个机会，让他们接受大规模统计回归和应用**机器学习的培训，以解决现实世界的工业问题。这些都是宝贵的技能，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。