Software projects are complex technical and organizational systems involving large numbers of artifacts and developers. To understand and tame software complexity, a wide variety of program analysis techniques have been developed for bug detection, program comprehension, verification, and more. At the same time, repository mining techniques aim at obtaining insights into the inner socio-technical workings of software projects at a larger scale. While both program analysis and repository mining have been successful on their own, they are largely isolated, which leaves considerable potential for synergies untapped. We present SEAL, the first integrated approach that combines low-level program analysis with high-level repository information. SEAL maps repository information, mined from the development history of a project, onto a low-level intermediate program representation, making it available for state-of-the-art program analysis. SEAL’s integrated approach allows us to efficiently address software engineering problems that span multiple levels of abstraction, from low-level data flow to high-level organizational information. To demonstrate its merits and practicality, we use SEAL to determine which code changes modify central parts of a given software project, how authors interact (indirectly) with each other through code, and we demonstrate that putting static analysis’ results into a socio-technical context improves their expressiveness and interpretability.

Function as a Service (FaaS) - the reason why so many practitioners and researchers talk about Serverless Computing - claims to hide all operational concerns. The promise when using FaaS is that users only have to focus on the core business functionality in form of cloud functions. However, a few configuration options remain within the developer's responsibility. Most of the currently available cloud function offerings force the user to choose a memory or other resource setting and a timeout value. CPU is scaled based on the chosen options. At a first glance, this seems like an easy task, but the tradeoff between performance and cost has implications on the quality of service of a cloud function. Therefore, in this paper we present a local simulation approach for cloud functions and support developers in choosing a suitable configuration. The methodology we propose simulates the execution behavior of cloud functions locally, makes the cloud and local environment comparable and maps the local profiling data to a cloud platform. This reduces time during the development and enables developers to work with their familiar tools. This is especially helpful when implementing multi-threaded cloud functions.