talk

Summary Software testing plays a crucial role in software development to improve the software’s consistency and performance. Since software testing activities demand considerable effort in the development process, many automated techniques have been introduced to aid de- velopers and testers in various testing phases, thereby reducing the costs related to these tasks. One category of these automated approaches seeks to generate software tests auto- matically using different strategies. One of the successful strategies, which is applied to industrial cases, uses metaheuristic search-based approaches for test generation automa- tion.

Summary Specialized tools, such as IDEs, issue trackers, and code review tools, are an indispensable part of the modern software engineering process. These tools are constantly evolving. Besides enabling tools to support a wider range of technologies and frameworks, we are learning to provide additional features in completely new ways. One prominent stream of innovation in software engineering tools is dedicated to utilizing historical data to enable data-driven features, such as defect prediction engines and recommender systems, which leverage records of prior activity to assist with decision making.

Summary Automated testing has become an essential process for improving the quality of software systems. Automated tests can help ensure that production code is robust under many usage conditions and that code meets performance and security needs. Nevertheless, writing effective tests is challenging and, unfortunately, often neglected. In the first part of this dissertation, we summarize and explain why test and production code are not treated with the same care, and we set our goal: we want to discover new techniques and tools to support developers when writing and reviewing code.

Summary Code review is a widely used technique to support software quality. It is a manual activity, often subject to repetitive and tedious tasks that increase the mental load of reviewers and compromise their effectiveness. The developer-centered nature of code review can represent a bottleneck that does not scale in large systems with the consequence of com- promising firms’ profits. This challenge has led to an entire line of research on code review improvement.

The main goal of this thesis is to investigate, improve and extend the applicability of mutation testing. To seek the potential directions of how to improve and extend the applicability of mutation testing, we have started with a systematic literature review on the current state of how mutation testing is applied. The results from the systematic literature review have further guided us towards three directions of research: speeding up mutation testing; deepening our understanding of mutation testing; exploring new application domains of mutation testing.

Continuous Delivery for Machine Learning (CD4ML) deals with the challenges of applying Continuous Delivery principles to ML systems to make the end-to-end process of developing and deploying them more repeatable and reliable. These systems are generally more complex than traditional software applications, and ML models are non-deterministic and hard to explain. In this talk we will discuss the challenges of testing and quality in ML systems, and share some practices for applying different types of tests to help overcome those issues.

Abstract: The Big Code team at Facebook explores the intersection of Machine Learning, Software Engineering, and Programming Languages, in order to create developer tools. This presentation will give an overview of our research, which includes topics such as code search/recommendation, program repair, test selection, and resolving crashes.