Predicting unstable software benchmarks using static source code features

Abstract

Software benchmarks are only as good as the performance measurements they yield. Unstable benchmarks show high variability among repeated measurements, which causes uncertainty about the actual performance and complicates reliable change assessment. However, if a benchmark is stable or unstable only becomes evident after it has been executed and its results are available. In this paper, we introduce a machine-learning-based approach to predict a benchmark’s stability without having to execute it. Our approach relies on 58 statically-computed source code features, extracted for benchmark code and code called by a benchmark, related to (1) meta information, e.g., lines of code (LOC), (2) programming language elements, e.g., conditionals or loops, and (3) potentially performance-impacting standard library calls, e.g., file and network input/output (I/O). To assess our approach’s effectiveness, we perform a large-scale experiment on 4,461 Go benchmarks coming from 230 open-source software (OSS) projects. First, we assess the prediction performance of our machine learning models using 11 binary classification algorithms. We find that Random Forest performs best with good prediction performance from 0.79 to 0.90, and 0.43 to 0.68, in terms of AUC and MCC, respectively. Second, we perform feature importance analyses for individual features and feature categories. We find that 7 features related to meta-information, slice usage, nested loops, and synchronization application programming interfaces (APIs) are individually important for good predictions; and that the combination of all features of the called source code is paramount for our model, while the combination of features of the benchmark itself is less important. Our results show that although benchmark stability is affected by more than just the source code, we can effectively utilize machine learning models to predict whether a benchmark will be stable or not ahead of execution. This enables spending precious testing time on reliable benchmarks, supporting developers to identify unstable benchmarks during development, allowing unstable benchmarks to be repeated more often, estimating stability in scenarios where repeated benchmark execution is infeasible or impossible, and warning developers if new benchmarks or existing benchmarks executed in new environments will be unstable.

A systematic mapping study of source code representation for deep learning in software engineering

“…We chose 2014 as a cutoff point because this was the year the TensorFlow system was initially released. I2: Making use of DL as a core contribution of the paper and explicitly reporting on the used code representation approach. To illustrate this criterion, we discuss the following study as a counterexample [26]. In this study, Laaber et al.…”

“…� I2: Making use of DL as a core contribution of the paper and explicitly reporting on the used code representation approach. To illustrate this criterion, we discuss the following study as a counterexample [26]. In this study, Laaber et al tackled an SE task (predictability of system performance) and the authors used an artificial neural network (ANN) as a DL model for that task.…”