There have been rapid recent developments in automated software test design, repair and program improvement. Advances in artificial intelligence also have great potential impact to tackle software testing research problems. In this paper we highlight open research problems and challenges from an industrial perspective. This perspective draws on our experience at Meta Platforms, which has been actively involved in software testing research and development for approximately a decade. As we set out here, there are many exciting opportunities for software testing research to achieve the widest and deepest impact on software practice. With this overview of the research landscape from an industrial perspective, we aim to stimulate further interest in the deployment of software testing research. We hope to be able to collaborate with the scientific community on some of these research challenges.

This paper outlines 4 open challenges for Software Testing in general and Search Based Software Testing in particular, arising from our experience with the Sapienz System Deployment at Facebook. The challenges may also apply more generally, thereby representing opportunities for the research community to further benefit from the growing interest in automated test design in industry.

Automated transplantation would open many exciting avenues for software development: suppose we could autotransplant code from one system into another, entirely unrelated, system. This paper introduces a theory, an algorithm, and a tool that achieve this. Leveraging lightweight annotation, program analysis identifies an organ (interesting behavior to transplant); testing validates that the organ exhibits the desired behavior during its extraction and after its implantation into a host. While we do not claim automated transplantation is now a solved problem, our results are encouraging: we report that in 12 of 15 experiments, involving 5 donors and 3 hosts (all popular real-world systems), we successfully autotransplanted new functionality and passed all regression tests. Autotransplantation is also already useful: in 26 hours computation time we successfully autotransplanted the H.264 video encoding functionality from the x264 system to the VLC media player; compare this to upgrading x264 within VLC, a task that we estimate, from VLC's version history, took human programmers an average of 20 days of elapsed, as opposed to dedicated, time.