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Register a new userTest Agents: Adaptive, Autonomous and Intelligent Test Cases
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Added by
Eduard Paul Enoiu
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Growth of software size, lack of resources to perform regression testing, and failure to detect bugs faster have seen increased reliance on continuous integration and test automation. Even with greater hardware and software resources dedicated to test automation, software testing is faced with enormous challenges, resulting in increased dependence on complex mechanisms for automated test case selection and prioritization as part of a continuous integration framework. These mechanisms are currently using simple entities called test cases that are concretely realized as executable scripts. Our key idea is to provide test cases with more reasoning, adaptive behavior and learning capabilities by using the concepts of intelligent software agents. We refer to such test cases as test agents. The model that underlie a test agent is capable of flexible and autonomous actions in order to meet overall testing objectives. Our goal is to increase the decentralization of regression testing by letting test agents to know for themselves when they should be executing, how they should update their purpose, and when they should interact with each other. In this paper, we envision software test agents that display such adaptive autonomous behavior. Emerging developments and challenges regarding the use of test agents are explored-in particular, new research that seeks to use adaptive autonomous agents in software testing.
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Test bots are automated testing tools that autonomously and periodically run a set of test cases that check whether the system under test meets the requirements set forth by the customer. The automation decreases the amount of time a development team spends on testing. As development projects become larger, it is important to focus on improving the test bots by designing more effective test cases because otherwise time and usage costs can increase greatly and misleading conclusions from test results might be drawn, such as false positives in the test execution. However, literature currently lacks insights on how test case design affects the effectiveness of test bots. This paper uses a case study approach to investigate those effects by identifying challenges in designing tests for test bots. Our results include guidelines for test design schema for such bots that support practitioners in overcoming the challenges mentioned by participants during our study.
Search-based test generation is guided by feedback from one or more fitness functions - scoring functions that judge solution optimality. Choosing informative fitness functions is crucial to meeting the goals of a tester. Unfortunately, many goals - such as forcing the class-under-test to throw exceptions, increasing test suite diversity, and attaining Strong Mutation Coverage - do not have effective fitness function formulations. We propose that meeting such goals requires treating fitness function identification as a secondary optimization step. An adaptive algorithm that can vary the selection of fitness functions could adjust its selection throughout the generation process to maximize goal attainment, based on the current population of test suites. To test this hypothesis, we have implemented two reinforcement learning algorithms in the EvoSuite unit test generation framework, and used these algorithms to dynamically set the fitness functions used during generation for the three goals identified above. We have evaluated our framework, EvoSuiteFIT, on a set of Java case examples. EvoSuiteFIT techniques attain significant improvements for two of the three goals, and show limited improvements on the third when the number of generations of evolution is fixed. Additionally, for two of the three goals, EvoSuiteFIT detects faults missed by the other techniques. The ability to adjust fitness functions allows strategic choices that efficiently produce more effective test suites, and examining these choices offers insight into how to attain our testing goals. We find that adaptive fitness function selection is a powerful technique to apply when an effective fitness function does not already exist for achieving a testing goal.
Software testing is an essential part of the software lifecycle and requires a substantial amount of time and effort. It has been estimated that software developers spend close to 50% of their time on testing the code they write. For these reasons, a long standing goal within the research community is to (partially) automate software testing. While several techniques and tools have been proposed to automatically generate test methods, recent work has criticized the quality and usefulness of the assert statements they generate. Therefore, we employ a Neural Machine Translation (NMT) based approach called Atlas(AuTomatic Learning of Assert Statements) to automatically generate meaningful assert statements for test methods. Given a test method and a focal method (i.e.,the main method under test), Atlas can predict a meaningful assert statement to assess the correctness of the focal method. We applied Atlas to thousands of test methods from GitHub projects and it was able to predict the exact assert statement manually written by developers in 31% of the cases when only considering the top-1 predicted assert. When considering the top-5 predicted assert statements, Atlas is able to predict exact matches in 50% of the cases. These promising results hint to the potential usefulness ofour approach as (i) a complement to automatic test case generation techniques, and (ii) a code completion support for developers, whocan benefit from the recommended assert statements while writing test code.
Unit testing represents the foundational basis of the software testing pyramid, beneath integration and end-to-end testing. Automated software testing researchers have proposed a variety of techniques to assist developers in this time-consuming task. In this paper we present an approach to support developers in writing unit test cases by generating accurate and useful assert statements. Our approach is based on a state-of-the-art transformer model initially pretrained on an English textual corpus. This semantically rich model is then trained in a semi-supervised fashion on a large corpus of source code. Finally, we finetune this model on the task of generating assert statements for unit tests. The resulting model is able to generate accurate assert statements for a given method under test. In our empirical evaluation, the model was able to predict the exact assert statements written by developers in 62% of the cases in the first attempt. The results show 80% relative improvement for top-1 accuracy over the previous RNN-based approach in the literature. We also show the substantial impact of the pretraining process on the performances of our model, as well as comparing it with assert auto-completion task. Finally, we demonstrate how our approach can be used to augment EvoSuite test cases, with additional asserts leading to improved test coverage.
Diversity has been used as an effective criteria to optimise test suites for cost-effective testing. Particularly, diversity-based (alternatively referred to as similarity-based) techniques have the benefit of being generic and applicable across different Systems Under Test (SUT), and have been used to automatically select or prioritise large sets of test cases. However, it is a challenge to feedback diversity information to developers and testers since results are typically many-dimensional. Furthermore, the generality of diversity-based approaches makes it harder to choose when and where to apply them. In this paper we address these challenges by investigating: i) what are the trade-off in using different sources of diversity (e.g., diversity of test requirements or test scripts) to optimise large test suites, and ii) how visualisation of test diversity data can assist testers for test optimisation and improvement. We perform a case study on three industrial projects and present quantitative results on the fault detection capabilities and redundancy levels of different sets of test cases. Our key result is that test similarity maps, based on pair-wise diversity calculations, helped industrial practitioners identify issues with their test repositories and decide on actions to improve. We conclude that the visualisation of diversity information can assist testers in their maintenance and optimisation activities.
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