Reinforcement Learning for Test Case Prioritization

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

Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review

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

A Search-Based Testing Approach for Deep Reinforcement Learning Agents

Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving and robotics. However, these algorithms have faced great challenges when deployed in safety-critical environments since they often exhibit erroneous behaviors that can lead to potentially critical errors. One way to assess the safety of DRL agents is to test them to detect possible faults leading to critical failures during their execution. This raises the question of how we can efficiently test DRL policies to ensure their correctness and adherence to safety requirements. Most existing works on testing DRL agents use adversarial attacks that perturb states or actions of the agent. However, such attacks often lead to unrealistic states of the environment. Their main goal is to test the robustness of DRL agents rather than testing the compliance of agents' policies with respect to requirements. Due to the huge state space of DRL environments, the high cost of test execution, and the black-box nature of DRL algorithms, the exhaustive testing of DRL agents is impossible. In this paper, we propose a Search-based Testing Approach of Reinforcement Learning Agents (STARLA) to test the policy of a DRL agent by effectively searching for failing executions of the agent within a limited testing budget. We use machine learning models and a dedicated genetic algorithm to narrow the search towards faulty episodes. We apply STARLA on Deep-Q-Learning agents which are widely used as benchmarks and show that it significantly outperforms Random Testing by detecting more faults related to the agent's policy. We also investigate how to extract rules that characterize faulty episodes of the DRL agent using our search results. Such rules can be used to understand the conditions under which the agent fails and thus assess its deployment risks

Immobilization of a new (salen) molybdenum(VI) complex onto the ion-exchangeable polysiloxane as a heterogeneous epoxidation catalyst

In this study, a new recoverable catalyst for the epoxidation of olefins was developed using a layered polysiloxane as a support for immobilizing  (salen) molybdenum(VI) complex by electrostatic interaction between the surface of the solid support and the electrically charged molybdenum complex. Characterization of the heterogeneous catalyst by Fourier transform infrared, XRD,1H NMR, and atomic absorption spectroscopes as well as thermogravimetric and CHN elemental analyses confirmed successful immobilization of the (salen) molybdenum(VI) complex to the support. The prepared catalyst catalyzed the epoxidation of olefins efficiently. The effect of different factors on the epoxidation of cyclooctene was investigated. Reaction conditions including reaction temperature, solvent type, substrate amount, catalyst amount and oxidant  amount  were systematically optimized in order to achieve the highest conversion of cyclooctene. Various other olefins showed high catalytic activity and selectivity under the optimal reaction conditions. Regenerability test demonstrated that the catalyst can be recycled for at least five times without leaching of molybdenum. Moreover, the catalyst showed good stability under the reaction conditions as determined by FT-IR and ICP-OES analyses

Scalable and Accurate Test Case Prioritization in Continuous Integration Contexts

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Bioresorbable Composite Polymeric Materials for Tissue Engineering Applications

This review covers the development of bioresorbable polymeric composites for applications in tissue engineering. Various commercially available bioresobable polymers are described, with emphasis on recent bioresorbable composites based on natural and synthetic polymers. Bioresorbable polymers contain hydrolyzable bonds, which are subjected to chemical degradation via either reactive hydrolysis or enzyme-catalyzed active hydrolysis. For synthetic polymers, chemical hydrolysis is the most important mode of degradation. The degradation rate can be controlled by varying the molecular weight and crystallinity. Examples of bioresorbable polymers are: polyurethane, poly(D,L)lactide, poly(lactic-co-glycolic) acid, poly(α-hydroxy acids), cross-linked polyester hydrogels, poly(orthoesters), polyanhydrides and polyethylene glycol

Stimulus-Responsive Polymeric Nanogels As Smart Drug Delivery Systems

Nanogels are three-dimensional nanoscale networks formed by physically or chemically cross-linking polymers. Nanogels have been explored as drug delivery systems due to their advantageous properties, such as biocompatibility, high stability, tunable particle size, drug loading capacity, and possible modification of the surface for active targeting by attaching ligands that recognize cognate receptors on the target cells or tissues. Nanogels can be designed to be stimulus responsive, and react to internal or external stimuli such as pH, temperature, light and redox, thus resulting in the controlled release of loaded drugs. This “smart” targeting ability prevents drug accumulation in non-target tissues and minimizes the side effects of the drug. This review aims to provide an introduction to nanogels, their preparation methods, and to discuss the design of various stimulus-responsive nanogels that are able to provide controlled drug release in response to particular stimuli