What are the challenges in developing and deploying software-based self-tests?5 answersDeveloping and deploying software-based self-tests face several challenges. One challenge is scaling existing software-based self-test capital to multithreaded chip multiprocessors (CMPs) and effectively detecting faults in functional components, thread-specific logic, and core interoperability logic. Another challenge is the need for tailored, enhanced, and scheduled uniprocessor self-test programs for CMPs built around mature microprocessor cores. Additionally, there is a need for open research problems and challenges to be addressed from an industrial perspective, such as automated test design, repair, and program improvement. Furthermore, there is a need to design systems that are easy to test, test systems modularly, determine when enough testing has been done, and define the criteria for completion.
How can AI be used to generate test cases that are more comprehensive and effective?4 answersAI can be used to generate more comprehensive and effective test cases. By utilizing AI algorithms, researchers can analyze extensive biological data and identify disease-associated targets, predicting their interactions with potential drug candidates. This targeted approach increases the likelihood of successful drug approvals. In the field of pathology, AI can support pathologists in their daily work and help discover novel biomarkers for improved patient care. In hepatology and pancreatology, AI has been applied to the interpretation of radiological images, providing accurate and reproducible diagnoses and reducing physicians' workload. In software testing, search-based software testing (SBST) guided by AI fitness functions has been shown to be effective in generating test cases automatically. Additionally, AI can be used to test the reliability of AI models, ensuring accuracy, fairness, and robustness.
Will generative ai replace automation in software testing?5 answersGenerative AI has the potential to revolutionize software testing, but it is unlikely to replace automation completely. Automated test-case generation using generative AI algorithms can enhance test coverage, improve efficiency, and ensure the quality of software products. Generative AI tools have gained widespread usage and can boost software engineering productivity. However, there are challenges such as data quality, bias, domain specificity, and the need for human expertise that need to be addressed. Generative scenario-based testing using QuickCheck implementation has shown promising results in revealing bugs and is suitable for testing interfaces and random user scenarios. A business process component-based framework enables test automation using a component generator and a script generator. Agile development methods can benefit from a formal framework for testing, including automation, to ensure quality software products.
How can researchers and software developers provide testing procedures for AI systems?3 answersResearchers and software developers can provide testing procedures for AI systems by applying software engineering and testing practices. The complex nature of AI systems, which are based on software, can benefit from established procedures in software engineering for assessing their behavior and characteristics. By connecting the key requirements of Trustworthy AI defined by the European Commission's AI high-level expert group with software engineering procedures, researchers and developers can assess the trustworthiness of AI systems. This connection allows for the application of software testing techniques to evaluate the correctness, robustness, and stiffness of AI systems. The use of machine learning and AI techniques in software testing can also aid in extracting features, classifying screen types and elements, and implementing test sequences for app testing. Reusing classifiers across applications and platforms can further enhance the testing process.
How can AI be used to test games?4 answersAI can be used to test games by employing generative AI agents that imitate playing behaviors and player experiences. These agents are trained using reinforcement learning paradigms and can exhibit distinctive play styles and experience responses of human players. By using AI agents, the process of play-testing games can be accelerated, providing valuable insights to game designers for improving game design. AI has been successfully used in various game genres, including board games, card games, first-person shooting games, and real-time strategy games. AI agents can analyze gameplay features, game boards, and rule variations, identifying effective playing styles and desirable game states. They can also reveal loopholes in game rules and provide trends in gameplay. AI-based playtesting can offer valuable information about modern board games and their designs in a cost-effective and time-efficient manner.
How is the efficacy of ai driven testing?5 answersThe efficacy of AI-driven testing is promising, with the potential to automate and improve the thoroughness of testing processes. AI techniques can be applied to generate test inputs and evaluate test outputs based on patterns learned from previous executions and knowledge gained from analyzing similar programs. AI planning approaches have been introduced for automated security testing, mapping attack models and security protocol definitions to generate test cases and automate test execution. AITEST is a comprehensive testing framework that extends its capability to include testing techniques for Image and Speech-to-text models, along with interpretability testing for tabular models. Rigorous system testing becomes even more important for safety-critical systems with AI methodologies, as it allows for the detection of critical scenarios that might otherwise be missed. Mathematical techniques for testing AI systems can be used as criteria to determine the adequacy of an AI system when no other models are available.