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. Our study demonstrates the potential of LLMs in improving the reliability of automated test generation tools, while also highlighting areas for future enhancement.

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.

Evolv-1 is a test case generation tool designed using Evolutionary Algorithms (EAs) to optimize UAV testing scenarios. This short paper presents Evolv-1's implementation as part of the ICST 2025 UAV Testing Tool Competition.

As video games become more complex and widespread, player experience (PX) testing becomes crucial in the game industry. Attracting and retaining players are key elements to guarantee the success of a game in the highly competitive market. Although a number of techniques have been introduced to measure the emotional aspect of the experience, automated testing of player experience still needs to be explored. This paper presents PX-MBT, a framework for automated player experience testing with emotion pattern verification. PX-MBT (1) utilizes a model-based testing approach for test suite generation, (2) employs a computational model of emotions developed based on a psychological theory of emotions to model players' emotions during game-plays with an intelligent agent, and (3) verifies emotion patterns given by game designers on executed test suites to identify PX-issues. We explain PX-MBT architecture and provide an example along with its result in emotion pattern verification, which asserts the evolution of emotions over time, and heat-maps to showcase the spatial distribution of emotions on the game map.

Abstract Player eXperience (PX) testing has attracted attention in the game industry as video games become more complex and widespread. Understanding players’ desires and their experience are key elements to guarantee the success of a game in the highly competitive market. Although a number of techniques have been introduced to measure the emotional aspect of the experience, automated testing of player experience still needs to be explored. This paper presents a framework for automated player experience testing by formulating emotion patterns’ requirements and utilizing a computational model of players’ emotions developed based on a psychological theory of emotions along with a model-based testing approach for test suite generation. We evaluate the strength of our framework by performing mutation test. The paper also evaluates the performance of a search-based generated test suite and LTL model checking-based test suite in revealing various variations of temporal and spatial emotion patterns. Results show the contribution of both algorithms in generating complementary test cases for revealing various emotions in different locations of a game level.

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.

MOTIVATION: Tumor purity (TP) is the proportion of cancer cells in a tumor sample. TP impacts on the accurate assessment of molecular and genomics features as assayed with NGS approaches. State-of-the-art tools mainly rely on somatic copy-number alterations (SCNA) to quantify TP and therefore fail when a tumor genome is nearly euploid, i.e. 'non-aberrant' in terms of identifiable SCNAs. RESULTS: We introduce a computational method, tumor purity estimation from single-nucleotide variants (SNVs), which derives TP from the allelic fraction distribution of SNVs. On more than 7800 whole-exome sequencing data of TCGA tumor samples, it showed high concordance with a range of TP tools (Spearman's correlation between 0.68 and 0.82; >9 SNVs) and rescued TP estimates of 1, 194 samples (15%) pan-cancer. AVAILABILITY AND IMPLEMENTATION: TPES is available as an R package on CRAN and at https://bitbucket.org/l0ka/tpes.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Important achievements in traditional biology has deepened the knowledge about living systems leading to an extensive identification of parts-list of the cell as well as of the interactions among biochemical species responsible for cell's regulation. Such an expanding knowledge also introduces new issues. For example the increasing comprehension of the inter- dependencies between pathways (pathways cross-talk) has resulted, on one hand, in the growth of informational complexity, on the other, in a strong lack of information coherence. The overall grand challenge remains unchanged: to be able to assemble the knowledge of every 'piece' of a system in order to figure out the behavior of the whole (integrative approach). In light of these considerations high performance computing plays a fundamental role in the context of in-silico biology. Stochastic simulation is a renowned analysis tool, which, although widely used, is subject to stringent computational requirements, in particular when dealing with heterogeneous and high dimensional systems. Here we introduce and discuss a methodology aimed at alleviating the burden of simulating complex biological networks. Such a method, which springs from graph theory, is based on the principle of fragmenting the computational space of a simulation trace and delegating the computation of fragments to a number of parallel processes.

The development of detailed, coherent, models of complex biological systems is recognized as a key requirement for integrating the increasing amount of experimental data. In addition, in-silico simulation of bio-chemical models provides an easy way to test different experimental conditions, helping in the discovery of the dynamics that regulate biological systems. However, the computational power required by these simulations often exceeds that available on common desktop computers and thus expensive high performance computing solutions are required. An emerging alternative is represented by general-purpose scientific computing on graphics processing units (GPGPU), which offers the power of a small computer cluster at a cost of approximately $400. Computing with a GPU requires the development of specific algorithms, since the programming paradigm substantially differs from traditional CPU-based computing. In this paper, we review some recent efforts in exploiting the processing power of GPUs for the simulation of biological systems.

Biological systems are characterised by a large number of interacting entities whose dynamics is described by a number of reaction equations. Mathematical methods for modelling biological systems are mostly based on a centralised solution approach: the modelled system is described as a whole and the solution technique, normally the integration of a system of ordinary differential equations (ODEs) or the simulation of a stochastic model, is commonly computed in a centralised fashion. In recent times, research efforts moved towards the definition of parallel/distributed algorithms as a means to tackle the complexity of biological models analysis. In this article, we present a survey on the progresses of such parallelisation efforts describing the most promising results so far obtained.