Empirical Evaluation of Mutation-based Test Prioritization Techniques

We propose a new test case prioritization technique that combines both mutation-based and diversity-based approaches. Our diversity-aware mutation-based technique relies on the notion of mutant distinguishment, which aims to distinguish one mutant's behavior from another, rather than from the original program. We empirically investigate the relative cost and effectiveness of the mutation-based prioritization techniques (i.e., using both the traditional mutant kill and the proposed mutant distinguishment) with 352 real faults and 553,477 developer-written test cases. The empirical evaluation considers both the traditional and the diversity-aware mutation criteria in various settings: single-objective greedy, hybrid, and multi-objective optimization. The results show that there is no single dominant technique across all the studied faults. To this end, \rev{we we show when and the reason why each one of the mutation-based prioritization criteria performs poorly, using a graphical model called Mutant Distinguishment Graph (MDG) that demonstrates the distribution of the fault detecting test cases with respect to mutant kills and distinguishment

Regression testing framework for test cases generation and prioritization

A regression test is a significant part of software testing. It is used to find the maximum number of faults in software applications. Test Case Prioritization (TCP) is an approach to prioritize and schedule test cases. It is used to detect faults in the earlier stage of testing environment. Code coverage is one of the features of a Regression Test (RT) that detects more number of faults from a software application. However, code coverage and fault detection are reducing the performance of existing test case prioritization by consuming a lot of time for scanning an entire code. The process of generating test cases plays an important role in the prioritization of test cases. The existing automated generation and prioritization techniques produces insufficient test cases that cause less fault detection rate or consumes more computation time to detect more faults. Unified Modelling Language (UML) based test case generation techniques can extract test cases from UML diagrams by covering maximum part of a module of an application. Therefore, a UML based test case generation can support a test case prioritization technique to find a greater number of faults with shorter execution time. A multi-objective optimization technique able to handle multiple objectives that supports RT to generate more number of test cases as well as increase fault detection rate and produce a better result. The aim of this research is to develop a framework to detect maximum number of faults with less execution time for improving the RT. The performance of the RT can be improved by an efficient test case generation and prioritization method based on a multi-objective optimization technique by handling both test cases and rate of fault detection. This framework consists of two important models: Test Case Generation (TCG) and TCP. The TCG model requires an UML use case diagram to extract test cases. A meta heuristic approach is employed that uses tokens for generating test cases. And, TCP receives the extracted test cases with faults as input to produce the prioritized set of test cases. The proposed research has modified the existing Hill Climbing based TCP by altering its test case swapping feature and detect faults in a reasonable execution time. The proposed framework intends to improve the performance of regression testing by generating and prioritizing test cases in order to find a greater number of faults in an application. Two case studies are conducted in the research in order to gather Test Case (TC) and faults for multiple modules. The proposed framework yielded a 92.2% of Average Percentage Fault Detection with less amount of testing time comparing to the other artificial intelligence-based TCP. The findings were proved that the proposed framework produced a sufficient amount of TC and found the maximum number of faults in less amount of time

Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration

Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available. This paper introduces Retecs, a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases. The Retecs method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history. In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the Retecs method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying Retecs on data extracted from three industrial case studies, we show for the first time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing.Comment: Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M. (2017). Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration. In Proceedings of 26th International Symposium on Software Testing and Analysis (ISSTA'17) (pp. 12--22). AC

Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning

The simultaneous charging of many electric vehicles (EVs) stresses the distribution system and may cause grid instability in severe cases. The best way to avoid this problem is by charging coordination. The idea is that the EVs should report data (such as state-of-charge (SoC) of the battery) to run a mechanism to prioritize the charging requests and select the EVs that should charge during this time slot and defer other requests to future time slots. However, EVs may lie and send false data to receive high charging priority illegally. In this paper, we first study this attack to evaluate the gains of the lying EVs and how their behavior impacts the honest EVs and the performance of charging coordination mechanism. Our evaluations indicate that lying EVs have a greater chance to get charged comparing to honest EVs and they degrade the performance of the charging coordination mechanism. Then, an anomaly based detector that is using deep neural networks (DNN) is devised to identify the lying EVs. To do that, we first create an honest dataset for charging coordination application using real driving traces and information revealed by EV manufacturers, and then we also propose a number of attacks to create malicious data. We trained and evaluated two models, which are the multi-layer perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the GRU detector gives better results. Our evaluations indicate that our detector can detect lying EVs with high accuracy and low false positive rate

Reduced Bias User Preference Methods for Determining the Pareto-Optimal Solution Point

Engineering design is filled with tradeoffs between competing objectives such as performance, mass, cost, and schedule. A designer must navigate these complex multi-objective problems and deliver the right solution for their application. Multi-objective optimization techniques are powerful and widely used; however, a key drawback to these techniques is that they often output a set of equivalent solutions called the Pareto Front. The designer must perform an additional multi-objective down selection on the Pareto Front to determine a single Pareto-optimal solution point for their design. Existing Pareto Front processing techniques either use traditional infinitely adjustable weights, which can yield results that are highly sensitive to the selected weights, or are no-preference methods that don’t account for the designers’ inputs on the relative importance of the objectives. These existing Pareto Front down selection methods do not provide a sufficient way for the designer to input their insight on the relative importance of the objectives without the technique being overly sensitiveto this input. This thesis proposes two new methods to find a Pareto-optimal solution point. These methods are called the Prioritize and Funnel algorithm and the Rank and Influence Based Weighting method. These methods build off existing strategies but vary from previously proposed techniques by accounting for the designers’ inputs through a designer ranking of the objectives. When applied to a series of example problems, these two new methods were demonstrated to efficiently perform the Pareto Front down selection and respond accordingly to changes in the designer preferences

Exploring Restart Distributions

We consider the generic approach of using an experience memory to help exploration by adapting a restart distribution. That is, given the capacity to reset the state with those corresponding to the agent's past observations, we help exploration by promoting faster state-space coverage via restarting the agent from a more diverse set of initial states, as well as allowing it to restart in states associated with significant past experiences. This approach is compatible with both on-policy and off-policy methods. However, a caveat is that altering the distribution of initial states could change the optimal policies when searching within a restricted class of policies. To reduce this unsought learning bias, we evaluate our approach in deep reinforcement learning which benefits from the high representational capacity of deep neural networks. We instantiate three variants of our approach, each inspired by an idea in the context of experience replay. Using these variants, we show that performance gains can be achieved, especially in hard exploration problems.Comment: RLDM 201