Fitsum Kifetew

Until recently the game industry heavily relied on manual playtesting to test the games it produces. Even if the benefits of introducing automated testing are acknowledged, it is rarely done in practice. Some of the main hurdles include the lack of automated testing tools that can target computer games as well as the complexity of automated game plays which are much more difficult to program than typical simple test sequences. This article presents an agent-based testing framework called aplib that comes with a Domain Specific Language (DSL) that allows complex playtests to be programmed more abstractly. A so-called goal structure is used to abstractly formulate a playtest scenario in terms of main goals and their decomposition into subgoals. Scenarios that are not too complicated can be formulated using static goal structures. More complex scenarios may need a test agent that can dynamically adapt its play according to the situation that evolves during the play. To handle such cases, aplib allows dynamic goals to be expressed as well. Invariants and pre-/post-conditions are used to assert the properties that a play is expected to satisfy. They include differential properties that allow constraints on the current state to be related to that of past states. Three case studies are included in the article. The first one aims to evaluate the performance of playtests programmed with aplib . The second shows that the approach can also be combined with other automated testing approaches, in this case reinforcement learning. The third shows the applicability of such playtests in a 3D setup and for non-functional testing.

This short report presents the 2025 edition of the Java Unit Testing Competition in which four test generation tools (EVOFUZZ, EVOSUITE, BBC, and RANDOOP) were benchmarked on a freshly selected set of 55 Java classes from six different open source projects. The benchmarking was based on structural metrics, such as code and mutation coverage of the classes under test, as well as on the readability of the generated test cases.

Recently testing of games via autonomous agents has shown great promise in tackling challenges faced by the game industry, which mainly relied on either manual testing or record/replay. In particular Reinforcement Learning (RL) solutions have shown potential by learning directly from playing the game without the need for human intervention. In this paper, we present cMarlTest, an approach for testing 3D games through curiosity driven Multi-Agent Reinforcement Learning (MARL). cMarlTest deploys multiple agents that work collaboratively to achieve the testing objective. The use of multiple agents helps resolve issues faced by a single agent approach. We carried out experiments on different levels of a 3D game comparing the performance of cMarlTest with a single agent RL variant. Results are promising where, considering three different types of coverage criteria, cMarlTest achieved higher coverage. cMarlTest was also more efficient in terms of the time taken, with respect to the single agent based variant.

Automated test generation is crucial for ensuring the reliability and robustness of software applications while at the same time reducing the effort needed. While significant progress has been made in test generation research, generating valid test oracles still remains an open problem. To address this challenge, we present AugmenTest, an approach leveraging Large Language Models (LLMs) to infer correct test oracles based on available documentation of the software under test. Unlike most existing methods that rely on code, AugmenTest utilizes the semantic capabilities of LLMs to infer the intended behavior of a method from documentation and developer comments, without looking at the code. AugmenTest includes four variants: Simple Prompt, Extended Prompt, RAG with a generic prompt (without the context of class or method under test), and RAG with Simple Prompt, each offering different levels of contextual information to the LLMs. To evaluate our work, we selected 142 Java classes and generated multiple mutants for each. We then generated tests from these mutants, focusing only on tests that passed on the mutant but failed on the original class, to ensure that the tests effectively captured bugs. This resulted in 203 unique tests with distinct bugs, which were then used to evaluate AugmenTest. Results show that in the most conservative scenario, AugmenTest's Extended Prompt consistently outperformed the Simple Prompt, achieving a success rate of 30\% for generating correct assertions. In comparison, the state-of-the-art TOGA approach achieved 8.2\%. Contrary to our expectations, the RAG-based approaches did not lead to improvements, with performance of 18.2\% success rate for the most conservative scenario.

Research in the area of automated test generation has seen remarkable progress in recent years, resulting in several approaches and tools for effective and efficient generation of test cases. In particular, the EvoSuite tool has been at the forefront of this progress embodying various algorithms for automated test generation of Java programs. EvoSuite has been used to generate test cases for a wide variety of programs as well. While there are a number of empirical studies that report results on the effectiveness, in terms of code coverage and other related metrics, of the various test generation strategies and algorithms implemented in EvoSuite, there are no studies, to the best of our knowledge, on the energy consumption associated to the automated test generation. In this paper, we set out to investigate this aspect by measuring the energy consumed by EvoSuite when generating tests. We also measure the energy consumed in the execution of the test cases generated, comparing them with those manually written by developers. The results show that the different test generation algorithms consumed different amounts of energy, in particular on classes with high cyclomatic complexity. Furthermore, we also observe that manual tests tend to consume more energy as compared to automatically generated tests, without necessarily achieving higher code coverage. Our results also give insight into the methods that consume significantly higher levels of energy, indicating potential points of improvement both for EvoSuite as well as the different programs under test.

Summary: Researchers and practitioners have designed and implemented various automated test case generators to support effective software testing. Such generators exist for various languages (e.g., Java, C#, or Python) and various platforms (e.g., desktop, web, or mobile applications). The generators exhibit varying effectiveness and efficiency, depending on the testing goals they aim to satisfy (e.g., unit‐testing of libraries versus system‐testing of entire applications) and the underlying techniques they implement. In this context, practitioners need to be able to compare different generators to identify the most suited one for their requirements, while researchers seek to identify future research directions. This can be achieved by systematically executing large‐scale evaluations of different generators. However, executing such empirical evaluations is not trivial and requires substantial effort to select appropriate benchmarks, setup the evaluation infrastructure, and collect and analyse the results. In this Software Note, we present ourJUnit Generation Benchmarking Infrastructure(JUGE) supporting generators (search‐based, random‐based, symbolic execution, etc.) seeking to automate the production of unit tests for various purposes (validation, regression testing, fault localization, etc.). The primary goal is to reduce the overall benchmarking effort, ease the comparison of several generators, and enhance the knowledge transfer between academia and industry by standardizing the evaluation and comparison process. Since 2013, several editions of a unit testing tool competition, co‐located with the Search‐Based Software Testing Workshop, have taken place whereJUGEwas used and evolved. As a result, an increasing amount of tools (over 10) from academia and industry have been evaluated onJUGE, matured over the years, and allowed the identification of future research directions. Based on the experience gained from the competitions, we discuss the expected impact ofJUGEin improving the knowledge transfer on tools and approaches for test generation between academia and industry. Indeed, theJUGEinfrastructure demonstrated an implementation design that is flexible enough to enable the integration of additional unit test generation tools, which is practical for developers and allows researchers to experiment with new and advanced unit testing tools and approaches.

Computer game is a billion-dollar industry and is booming. Testing games has been recognized as a difficult task, which mainly relies on manual playing and scripting based testing. With the advances in technologies, computer games have become increasingly more interactive and complex, thus play-testing using human participants alone has become unfeasible. In recent days, play-testing of games via autonomous agents has shown great promise by accelerating and simplifying this process. Reinforcement Learning solutions have the potential of complementing current scripted and automated solutions by learning directly from playing the game without the need of human intervention. This paper presented an approach based on reinforcement learning for automated testing of 3D games. We make use of the notion of curiosity as a motivating factor to encourage an RL agent to explore its environment. The results from our exploratory study are promising and we have preliminary evidence that reinforcement learning can be adopted for automated testing of 3D games.