Deploying Search Based Software Engineering with Sapienz at Facebook

We describe the deployment of the Sapienz Search Based Software Engineering (SBSE) testing system. Sapienz has been deployed in production at Facebook since September 2017 to design test cases, localise and triage crashes to developers and to monitor their fixes. Since then, running in fully continuous integration within Facebook’s production development process, Sapienz has been testing Facebook’s Android app, which consists of millions of lines of code and is used daily by hundreds of millions of people around the globe. We continue to build on the Sapienz infrastructure, extending it to provide other software engineering services, applying it to other apps and platforms, and hope this will yield further industrial interest in and uptake of SBSE (and hybridisations of SBSE) as a result

Inspecting Code Churns to Prioritize Test Cases

Within the context of software evolution, due to time-to-market pressure, it is not uncommon that a company has not enough time and/or resources to re-execute the whole test suite on the new software version, to check for non-regression. To face this issue, many Regression Test Prioritization techniques have been proposed, aimed at ranking test cases in a way that tests more likely to expose faults have higher priority. Some of these techniques exploit code churn metrics, i.e. some quantification of code changes between two subsequent versions of a software artifact, which have been proven to be effective indicators of defect-prone components. In this paper, we first present three new Regression Test Prioritization strategies, based on a novel code churn metric, that we empirically assessed on an open source software system. Results highlighted that the proposal is promising, but that it might be further improved by a more detailed analysis on the nature of the changes introduced between two subsequent code versions. To this aim, in this paper we also sketch a more refined approach we are currently investigating, that quantifies changes in a code base at a finer grained level. Intuitively, we seek to prioritize tests that stress more fault-prone changes (e.g., structural changes in the control flow), w.r.t. those that are less likely to introduce errors (e.g., the renaming of a variable). To do so, we propose the exploitation of the Abstract Syntax Tree (AST) representation of source code, and to quantify differences between ASTs by means of specifically designed Tree Kernel functions, a type of similarity measure for tree-based data structures, which have shown to be very effective in other domains, thanks to their customizability