Modern software systems often exhibit numerous configuration options to tailor them to user requirements, including the system's performance behavior. Performance models derived via machine learning are an established approach for estimating and optimizing configuration-dependent software performance. Most existing approaches in this area rely on software performance measurements conducted with a single workload (i.e., input fed to a system). This single workload, however, is often not representative of a software system's real-world application scenarios. Understanding to what extent configuration and workload-individually and combined-cause a software system's performance to vary is key to understand whether performance models are generalizable across different configurations and workloads. Yet, so far, this aspect has not been systematically studied. To fill this gap, we conducted a systematic empirical study across 25 258 configurations from nine real-world configurable software systems to investigate the effects of workload variation at system-level performance and for individual configuration options. We explore driving causes for workload-configuration interactions by enriching performance observations with option-specific code coverage information. Our results demonstrate that workloads can induce substantial performance variation and interact with configuration options, often in non-monotonous ways. This limits not only the generalizability of single-workload models, but also challenges assumptions for existing transfer-learning techniques. As a result, workloads should be considered when building performance prediction models to maintain and improve representativeness and reliability.

Reducing energy consumption of software is an increasingly important objective, and there has been extensive research for data centers, smartphones, and embedded systems. However, when it comes to software, we lack working tools and methods to directly reduce energy consumption. For performance, we can resort to configuration options for tuning response time or throughput of a software system. For energy, it is still unclear whether the underlying assumption that runtime performance correlates with energy consumption holds, especially when it comes to optimization via configuration. To evaluate whether and to what extent this assumption is valid for configurable software systems, we conducted the largest empirical study of this kind to date. First, we searched the literature for reports on whether and why runtime performance correlates with energy consumption. We obtained a mixed, even contradicting picture from positive to negative correlation, and that configurability has not been considered yet as a factor for this variance. Second, we measured and analyzed both the runtime performance and energy consumption of 14 real-world software systems. We found that, in many cases, it depends on the software system's configuration whether runtime performance and energy consumption correlate and that, typically, only few configuration options influence the degree of correlation. A fine-grained analysis at the function level revealed that only few functions are relevant to obtain an accurate proxy for energy consumption and that, knowing them, allows one to infer individual transfer factors between runtime performance and energy consumption.

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.

Global variables make software systems hard to maintain and debug, and break local reasoning. They also impose a non-trivial challenge to static analysis which needs to model its effects to obtain sound analysis results. However, global variable initialization, codes of corresponding constructors and destructors as well as dynamic library code executed during load and unload not only affect control flows but data flows, too. The PhASAR static data-flow analysis framework does not handle these special cases and also does not provide any functionalities to model the effects of globals. Analysis writers are forced to model the desired effects in an ad-hoc manner increasing an analysis’ complexity and imposing an additional repetitive task. In this paper, we present the challenges of modeling globals, elaborate on the impact they have on analysis information, and present a suitable model to capture their effects, allowing for an easier development of global-aware static data-flow analyses. We present an implementation of our model within the PhASAR framework and show its usefulness for an IDE-based linear-constant propagation that crucially requires correct modeling of globals for correctness.