Software analytics

Abstract

Nowadays, software development projects produce a large number of software artifacts including source code, execution traces, end-user feedback, as well as informal documentation such as developers' discussions, change logs, StackOverflow, and code reviews. Such data embeds rich and significant knowledge about software projects, their quality and services, as well as the dynamics of software development. Most often, this data is not organized, stored, and presented in a way that is immediately useful to software developers and project managers to support their decisions. To help developers and managers understand their projects, how they evolve, as well as support them during their decision-making process, software analytics -- use of analysis, data, and systematic reasoning for making decisions -- has become an emerging field of modern data analysis. While results obtained from analytics-based solutions suggested so far are promising, there are still several challenges associated with the adoption of software analytics into software development processes, as well as the development and integration of analytics tools in practical settings. We therefore propose a tutorial on software analytics. The tutorial will start with an introduction of software analytics. Next, we will discuss the main challenges and opportunities associated with software analytics based on the examples from our own research. These examples will cover a range of topics leveraging software analytics. The topics include mobile apps quality, code review process and its quality, analytics for the software engineering Twitter space, as well as the use of analytics to solve scheduling problems in the cloud.