Mojtaba Bagherzadeh

Ahmadreza Saboor Yaraghi, Mojtaba Bagherzadeh, Nafiseh Kahani et al.

Continuous Integration (CI) requires efficient regression testing to ensure software quality without significantly delaying its CI builds. This warrants the need for techniques to reduce regression testing time, such as Test Case Prioritization (TCP) techniques that prioritize the execution of test cases to detect faults as early as possible. Many recent TCP studies employ various Machine Learning (ML) techniques to deal with the dynamic and complex nature of CI. However, most of them use a limited number of features for training ML models and evaluate the models on subjects for which the application of TCP makes little practical sense, due to their small regression testing time and low number of failed builds. In this work, we first define, at a conceptual level, a data model that captures data sources and their relations in a typical CI environment. Second, based on this data model, we define a comprehensive set of features that covers all features previously used by related studies. Third, we develop methods and tools to collect the defined features for 25 open-source software systems with enough failed builds and whose regression testing takes at least five minutes. Fourth, relying on the collected dataset containing a comprehensive feature set, we answer four research questions concerning data collection time, the effectiveness of ML-based TCP, the impact of the features on effectiveness, the decay of ML-based TCP models over time, and the trade-off between data collection time and the effectiveness of ML-based TCP techniques.

Rongqi Pan, Mojtaba Bagherzadeh, Taher A. Ghaleb et al.

Regression testing is an essential activity to assure that software code changes do not adversely affect existing functionalities. With the wide adoption of Continuous Integration (CI) in software projects, which increases the frequency of running software builds, running all tests can be time-consuming and resource-intensive. To alleviate that problem, Test case Selection and Prioritization (TSP) techniques have been proposed to improve regression testing by selecting and prioritizing test cases in order to provide early feedback to developers. In recent years, researchers have relied on Machine Learning (ML) techniques to achieve effective TSP (ML-based TSP). Such techniques help combine information about test cases, from partial and imperfect sources, into accurate prediction models. This work conducts a systematic literature review focused on ML-based TSP techniques, aiming to perform an in-depth analysis of the state of the art, thus gaining insights regarding future avenues of research. To that end, we analyze 29 primary studies published from 2006 to 2020, which have been identified through a systematic and documented process. This paper addresses five research questions addressing variations in ML-based TSP techniques and feature sets for training and testing ML models, alternative metrics used for evaluating the techniques, the performance of techniques, and the reproducibility of the published studies. We summarize the results related to our research questions in a high-level summary that can be used as a taxonomy for classifying future TSP studies.

Mojtaba Bagherzadeh, Nafiseh Kahani, Karim Jahed et al.

The iterative and incremental nature of software development using models typically makes a model of a system incomplete (i.e., partial) until a more advanced and complete stage of development is reached. Existing model execution approaches (interpretation of models or code generation) do not support the execution of partial models. Supporting the execution of partial models at the early stages of software development allows early detection of defects, which can be fixed more easily and at a lower cost. This paper proposes a conceptual framework for the execution of partial models, which consists of three steps: static analysis, automatic refinement, and input-driven execution. First, a static analysis that respects the execution semantics of models is applied to detect problematic elements of models that cause problems for the execution. Second, using model transformation techniques, the models are refined automatically, mainly by adding decision points where missing information can be supplied. Third, refined models are executed, and when the execution reaches the decision points, it uses inputs obtained either interactively or by a script that captures how to deal with partial elements. We created an execution engine called PMExec for the execution of partial models of UML-RT (i.e., a modeling language for the development of soft real-time systems) that embodies our proposed framework. We evaluated PMExec based on several use-cases that show that the static analysis, refinement, and application of user input can be carried out with reasonable performance and that the overhead of approach, which is mostly due to the refinement and the increase in model complexity it causes, is manageable. We also discuss the properties of the refinement formally and show how the refinement preserves the original behaviors of the model.

Mojtaba Bagherzadeh, Nafiseh Kahani, Lionel Briand

Continuous Integration (CI) significantly reduces integration problems, speeds up development time, and shortens release time. However, it also introduces new challenges for quality assurance activities, including regression testing, which is the focus of this work. Though various approaches for test case prioritization have shown to be very promising in the context of regression testing, specific techniques must be designed to deal with the dynamic nature and timing constraints of CI. Recently, Reinforcement Learning (RL) has shown great potential in various challenging scenarios that require continuous adaptation, such as game playing, real-time ads bidding, and recommender systems. Inspired by this line of work and building on initial efforts in supporting test case prioritization with RL techniques, we perform here a comprehensive investigation of RL-based test case prioritization in a CI context. To this end, taking test case prioritization as a ranking problem, we model the sequential interactions between the CI environment and a test case prioritization agent as an RL problem, using three alternative ranking models. We then rely on carefully selected and tailored state-of-the-art RL techniques to automatically and continuously learn a test case prioritization strategy, whose objective is to be as close as possible to the optimal one. Our extensive experimental analysis shows that the best RL solutions provide a significant accuracy improvement over previous RL-based work, with prioritization strategies getting close to being optimal, thus paving the way for using RL to prioritize test cases in a CI context.

Hazem Fahmy, Fabrizio Pastore, Mojtaba Bagherzadeh et al.

Deep neural networks (DNNs) are increasingly important in safety-critical systems, for example in their perception layer to analyze images. Unfortunately, there is a lack of methods to ensure the functional safety of DNN-based components. We observe three major challenges with existing practices regarding DNNs in safety-critical systems: (1) scenarios that are underrepresented in the test set may lead to serious safety violation risks, but may, however, remain unnoticed; (2) characterizing such high-risk scenarios is critical for safety analysis; (3) retraining DNNs to address these risks is poorly supported when causes of violations are difficult to determine. To address these problems in the context of DNNs analyzing images, we propose HUDD, an approach that automatically supports the identification of root causes for DNN errors. HUDD identifies root causes by applying a clustering algorithm to heatmaps capturing the relevance of every DNN neuron on the DNN outcome. Also, HUDD retrains DNNs with images that are automatically selected based on their relatedness to the identified image clusters. We evaluated HUDD with DNNs from the automotive domain. HUDD was able to identify all the distinct root causes of DNN errors, thus supporting safety analysis. Also, our retraining approach has shown to be more effective at improving DNN accuracy than existing approaches.