The University of Oslo Library provides various guidance services, including literature searching, referencing, and academic writing support. Many students struggle to understand and use these services. They are often unaware of them, not expecting that the library offers that kind of assistance. The primary goal of our project was to design a guidance service that is accessible, easily understandable, and tailored to the students’ needs. In this project we have used User Experience (UX) methods. UX methods are a range of techniques used to understand user behavior, needs, and preferences to improve the design and usability of products and services. Our approach incorporated both qualitative and quantitative methods, including guerrilla testing, semi-structured interviews, prototyping, surveys, usability testing, and idea generation. Throughout the process, we actively engaged both students and guidance providers, focusing on creating user-friendly services. We explored user needs, tested alternative forms of guidance, and implemented a new system for booking guidance sessions. Developing new websites has also been a part of the project. Our findings underscore the importance of engaging with students and involving them in the development and design of library services. Their unique perspectives are invaluable for our decision-making processes, and for gaining a deeper understanding of their needs. This project has made it clear that library services must be easily accessible, highly visible, and approachable. Currently, many students find it challenging to seek help, and it is therefore essential that these services are recommended to them by someone they deem trustworthy and credible.

The paper presents algorithm for the automatic generation of thematic tests using the example of English language tests using the counterfactual analysis method to improve their quality based on a mobile application. A detailed analysis of the language domain led to the development of clear requirements for the future service. Key forms of assessment knowledge were classified, along with descriptions of typical exercises and the difficulty levels in which they are used, helping to create a comprehensive picture of the skills requiring step-by-step assessment. The challenges of existing tests are highlighted: ambiguous wording, multiple correct answers, and labor-intensive selection. This paper develops and tests a comprehensive approach to assessing the effectiveness of prompts for generating grammar tests based on Large Language Models. A counterfactual algorithm is proposed as a core, which allows identifying latent features that actually influence the choice of grammatical structures of the model, selectively modifying the prompt, and evaluating changes using three complementary metrics. The application of the algorithm showed that adding explicit indications of the most significant hidden features increases the model's sensitivity to key factors of the task. Further re-evaluation of quality using the developed metrics and independent expert review confirmed a statistically significant increase (p < 0.01) in both grammatical compliance and compliance with the structure of tasks: the average score increased from 0,91 to 0,95. Thus, counterfactual analysis is indeed an effective tool for fine-tuning prompts; the proposed improved prompt ensures more reliable generation of test materials that meet educational standards and lays the foundation for scaling the algorithm to other types of tasks and language skills.

Target product profile to guide development of next generation diagnostic test for Salmonella enterica: Responding to the crisis of drug resistant typhoid.

Modern Large Language Model (LLM)-based programming agents often rely on test execution feedback to refine their generated code. These tests are synthetically generated by LLMs. However, LLMs may produce invalid or hallucinated test cases, which can mislead feedback loops and degrade the performance of agents in refining and improving code. This paper introduces <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VALTEST</monospace>, a novel framework that leverages semantic entropy to automatically validate test cases generated by LLMs. By analyzing the semantic structure of test cases and computing entropy-based uncertainty measures, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VALTEST</monospace> trains a machine learning model to classify test cases as valid or invalid and filters out invalid test cases. Experiments on multiple benchmark datasets and various LLMs show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VALTEST</monospace> not only boosts test validity by up to 29% but also improves code generation performance, as evidenced by significant increases in pass@1 scores. Our extensive experiments also reveal that semantic entropy is a reliable indicator to distinguish between valid and invalid test cases and provides a robust solution for improving the correctness of LLM-generated test cases used in software testing and code generation.

Simulation-based testing is essential for evaluating the safety of Autonomous Driving Systems (ADSs). Comprehensive evaluation requires testing across diverse scenarios that can trigger various types of violations under different conditions. While existing methods typically focus on individual diversity metrics, such as input scenarios, ADS-generated motion commands, and system violations, they often fail to capture the complex interrelationships among these elements. For instance, identical motion commands can produce different collision risks in varying scenes, and the same collision may result from different commands under different scenarios. This oversight leads to gaps in testing coverage, potentially missing critical issues in the ADS under evaluation. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Causal-Fuzzer</monospace>, the first causality-aware fuzzing technique that enables efficient and comprehensive testing of ADSs by constructing causal graphs to model the interrelationships among scenarios, actions, and violations. Unlike existing methods that treat diversity metrics independently, we recognize these elements are causally interconnected and use their relationships to identify more diverse violations triggered by fundamentally different causal mechanisms. Specifically, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Causal-Fuzzer</monospace> proposes (1) a causality-based feedback mechanism that quantifies the combined diversity of test scenarios by assessing whether they activate new causal relationships, and (2) a causality-driven mutation strategy that prioritizes mutations on input scenario elements with higher causal impact on ego action changes and violation occurrence to enable interpretable and efficient test generation. We evaluated <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Causal-Fuzzer</monospace> on an industry-grade ADS Apollo, with a high-fidelity simulator LGSVL. Our empirical results demonstrate that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Causal-Fuzzer</monospace> significantly outperforms existing methods in (1) identifying a greater diversity of violations (96.5 violations on average, compared to 66.9 for the best baseline method), (2) providing enhanced testing sufficiency with improved coverage of causal relationships (13.6 unique sceneaction- violation patterns on average, compared to 8.6 for the best baseline method), and (3) achieving greater efficiency in detecting critical scenarios, strong robustness under noise conditions, and good generalizability across varying scenario complexities and violation types. Our source code and experimental results are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/causal-fuzzer</uri>.

There is extensive literature on automated test generation using reactive design models, where control is determined by events. In contrast, the (idealised) simulation paradigm defines control through cycles dictated by the passage of time. Within each cycle, inputs are read and processed, and outputs are provided, all instantaneously, and afterwards time progresses. To exercise a simulation using tests generated from a reactive design model requires changes to the tests to take into account this paradigm shift. This paper focuses on automation of the necessary changes and of the use of the resulting tests in a simulation campaign. Based on a notion of conformance that establishes whether a simulation is correct with respect to a reactive design, we (1) identify the reactive tests that are meaningful; (2) define a process to convert those tests; (3) provide an algorithm to execute those tests; and (4) prove soundness and completeness of our approach. Our work is described in the context of the RoboStar framework for model-based development of control software for robotics applications, and its process algebraic semantics. The testing approach we propose here represents a significant advancement in the current testing practices within the field of robotics, where simulations are widely used.

Verification is critical in ensuring the reliable operation of modern, complex computing systems. However, as processor designs become increasingly sophisticated, conventional static verification techniques struggle to generate high-quality test sequences that achieve comprehensive coverage. Dynamic simulation-based approaches, which leverage coverage-driven objectives, can increase confidence in correct processor functionality but often suffer from low verification efficiency due to the generation of redundant test sequences and significant computational overhead. To address these challenges, this paper presents DeepVerifier, a novel coverage-guided test generation framework that leverages data-driven learning of existing test sequences and their associated coverage feedback. DeepVerifier uses a language model to learn the semantic representations of test sequences, ensure adherence to syntax constraints, and estimate the relationship between test sequences and coverage scores. By updating test sequences with higher coverage, DeepVerifier can significantly improve the efficiency and effectiveness of the verification process. Experimental results of verifying an out-of-order RISC-V microprocessor demonstrate that the framework accurately estimates the coverage scores of test sequences and updates high-quality sequences that contribute to higher coverage. This coverage-guided test generation technique holds promise for enhancing the reliability of modern processor designs.

Recently, Large Language Models (LLMs) have shown promising results in code generation, and several automated test generation approaches based on LLMs have been proposed. Although these approaches achieve promising performance, they suffer from two limitations. First, they lack the intrinsic understanding of the semantic intricacies and logical constructs inherent to the focal method. Second, they ignore the diversity of the generated tests and generate tests with limited code coverage. To alleviate these two limitations, in this work, we propose a novel approach named TestCTRL that optimizes LLMs for unit test generation by the Chain-of-Thought (CoT) prompt and Reinforcement Learning (RL) strategy. Specifically, we first build a new CoT dataset, containing the focal methods, corresponding unit tests, and CoT prompts. The CoT prompt includes the intention and possible test input values. Then, the CoT dataset is used to fine-tune one LLM (i.e., CodeLlama 7B) that can be seen as the policy model in RL. Meanwhile, we fine-tune another LLM (i.e., CodeGPT) as the reward model by predicting the line coverage of the focal method and its test. Moreover, we employ the Proximal Policy Optimization (PPO) algorithm to optimize the policy model and generate unit tests. We use the Defects4J benchmark to evaluate our approach from three perspectives (i.e., naturalness, validity, and code coverage). To avoid data leakage threats, we filtered out data from the CoT dataset that have the same focal method and test case names as those in the Defects4J. The experimental results demonstrate that TestCTRL outperforms state-of-the-art baselines in line and branch coverages, respectively. Besides, TestCTRL improves bug detection performance. We also investigate the reason for the proposed approach’s superiority.

Exposure of cells or tissues to chemical compounds can be analyzed through transcriptomic signatures, which can be used to classify chemical agents. This information can also enrich Adverse Outcome Pathways (AOP). Transcriptional signatures have generally been obtained using "bulk" analysis, by which the global gene expression pattern of an entire tissue is determined. Although this approach has been useful in toxicology, some information is lost, especially when tissues containing multiple cell types are considered. With the advent of single-cell transcriptomics (scRNA-seq), it is now possible to obtain higher resolution, cell type-specific responses in complex tissues. The aim of the present study was to evaluate the added value of scRNA-seq in analysis of the acute response of human bronchial epithelial cells grown at the air/liquid interface (ALI) to a known toxic compound, CdCl2, with well described transcriptional signatures of exposure. Fully differentiated mucocilliary epithelia obtained from three independent donors were exposed to 10µM CdCl2 and scRNA-seq analysis was performed on a total of 18,255 cells to obtain cell type-specific signatures. Our results show that the contribution of each cell type to the overall transcriptomic bulk response varies. For example, the classical heavy metal detoxification response was only detected in multiciliated and secreting cells, while absent in basal cells. The data demonstrate that scRNA-seq provides high-resolution transcriptional signatures with unexpected features. This added information is likely to have implications for the refinement of AOPs and could serve as a basis for a new generation of tests in predictive toxicology.

This paper introduces an innovative no-code methodology called QUALITY for automated test generation utilizing Excel templates for unit and integration testing. The suggested method allows non-technical stakeholders to engage in test creation while upholding software quality standards. Utilizing familiar Excel interface enables enterprises to lower the entry barriers for test automation and enhance test coverage among development teams. This technique connects business requirements with technical testing, thereby expediting software delivery while ensuring quality assurance. • No-Code Test Generation: QUALITY transforms Excel-based templates into operational API unit and integration tests without necessitating programming proficiency. • Multi-Protocol Support: QUALITY provides native support for REST, SOAP, and GraphQL, facilitating cohesive testing across many API environments. • Template-Driven Maintainability: Test cases can be modified or expanded effortlessly by adjusting spreadsheet rows, hence minimizing maintenance burdens. • Integration and Reporting: QUALITY facilitates dependency-aware execution, CI/CD integration (maven supported), and produces comprehensive HTML, CSV, Console Logs and Extent reports for actionable insights.

A modular soft core-based system for affordable acquisition and processing of electrophysiological recordings

Background: The high rate of user interface (UI) and source code changes in contemporary software development resulted in automated testing failures that augmented maintenance expenses and decreased the usefulness of automated testing. The current tools need regular updating by manual means, which is ineffective and expensive. Purpose: To present the Adaptive Scriptless Behavior-Driven Development (BDD) Automation Framework with Self-Healing Intelligence, which is an artificial intelligence (AI) and machine learning (ML)-driven framework of automatic test failure detection and resolution based on UI drift, broken locators, or timing. Approaches: The framework uses dynamic locator approaches, adaptive test generation, and reinforcement learning to allow updating test scripts in response to application changes. Such a self-healing feature will minimize human intervention and reduce maintenance expenses. An experimental case study was conducted in order to assess the performance of the framework in a practical context. Findings: The framework demonstrated significant advances in automated testing, such as a 30% drop in maintenance speed, reduced number of resources to update tests, a 25 % reduction in total cost of testing since less manual effort is needed, and a 40 % rise in stability of the test suites, which can execute its tests more reliably and with greater accuracy despite the presence of changes to the application. Conclusions: The Adaptive Scriptless BDD Automation Framework with Self-Healing Intelligence goes a long way to improving the flexibility, scalability, and efficiency of automated testing. It enhances the speed of testing, saves costs, and adds confidence in the quality of software, and is therefore valuable for ensuring high-quality standards in dynamic software landscapes.

We propose and model a flexible polarization-modulation-based microwave photonic waveform generator capable of synthesizing versatile waveform profiles with high fidelity. The architecture employs two Mach-Zehnder modulators (MZMs) biased to work as phase modulators, which are driven by RF signals, strategically interleaved with optical bias elements and terminated with a polarizer to convert polarization dynamics into intensity modulation. An analytical model is developed to describe the polarization evolution and its dependency on the system parameters, enabling predictive control over the generated waveforms. Using this model, we demonstrate the generation of four key waveform types-triangular, square, fully rectified sinusoidal waveforms, and sawtooth waveform-through bias tuning and signal phasing. This polarization-modulation technique introduces a tunable, bias-controlled degree of freedom for optical-to-electrical waveform shaping, opening what we believe are new pathways for integrated, reconfigurable microwave photonic systems in beamforming, testing, and arbitrary waveform generation applications.

Activity Streams is a data format designed to describe activities. Although its specification is written in natural language, its core vocabulary is formally defined in an OWL (Web Ontology Language) ontology. In this work, we propose a set of additional OWL axioms to extend the existing ontology, with the goal of enabling more detailed, precise, and machine-interpretable descriptions of Activity Streams. This enhancement aims to support real-world applications, such as those related to automatic test generation from OWL specifications.

Logic-level defects that escape manufacturing tests pose reliability risks in modern systems and require functional testing to identify their activation and propagation behaviors. However, effective functional testing is limited by the high cost of long-cycle fault simulations. To address this challenge, we propose a Spatio-Temporal Graph Convolutional Network framework to efficiently and accurately predict the Fault Impact Probability on the circuit's function cross-over multiple function cycles, enabling rapid quantitative assessment of functionally possible faults. Our method represents gate-level netlists as spatio-temporal graphs, capturing both structural connectivity and short-range signal-propagation dynamics. With dedicated spatial and temporal encoders, the proposed ST-GCN enables accurate prediction of multi-cycle circuit-level FIP. Experiments on ISCAS’89 benchmarks show that the approach reduces fault-simulation cost by over an order of magnitude while maintaining high accuracy (mean absolute error as low as 0.024 for 5-cycle predictions). The framework supports both testability-metric-based and simulation-based feature construction, enabling a tunable balance between efficiency and accuracy. A case study on test point selection further demonstrates that using predicted FIPs to guide observation-point placement improves the detectability of multi-cycle, hard-to-detect circuit-level faults. Overall, this work provides a scalable solution for circuit-level multi-cycle fault-impact assessment and can be readily integrated into functional test generation and other Electronic Design Automation workflows.

Chiplet-based designs use large numbers of interconnects that need to be tested thoroughly. Standard isolation logic allows the logic blocks (chiplets) and the interconnects to be tested separately. It was recently suggested for additional defect coverage to use a scan-based test set that tests the interconnects together with the logic blocks in a mode of operation that is closer to functional. In this scenario, a scan-based test set for a logic block targets faults in the logic block as well as the interconnects it drives. An exhaustive static fault model was used earlier for subsets of adjacent interconnects. In the same scenario, this article studies the presence of functionally undetectable interconnect faults, and their relationship to the configuration of the interconnects as a two-dimensional array. The article observes that the specific configuration of the interconnects in the two-dimensional array can affect the number of functionally undetectable faults. Moreover, by modifying the configuration, it is possible to eliminate functionally undetectable faults that are important to consider in other configurations. The article describes a test generation procedure that includes the identification of functionally undetectable interconnect faults, and a procedure for reconfiguring the interconnects to eliminate undetectable faults. The implementation of the procedures was carried out in an academic simulation environment. Experimental results for benchmark circuits demonstrate the effectiveness of the procedures in achieving complete interconnect fault coverage, and eliminating all the undetectable interconnect faults.

The software industry increasingly emphasizes the improvement of software quality. Modern development practices recognize that software should be built and maintained through a well-defined, structured process, consisting of organized and systematic steps for designing, developing, and evolving software systems. Petri net is applied for basic block of the software process. Path-based testing supports the software process by systematically identifying key execution paths to improve test planning and coverage. Using optimization techniques like Particle Swarm Optimization (PSO) helps efficiently generate test paths, enhancing the quality and effectiveness of process-level testing activities. Search-based optimization techniques improve decision making in the software process by transforming tasks such as cost estimation, release planning, and test generation into optimization problems. These methods efficiently explore large solution spaces to find near optimal solutions. Genetic algorithm (GA) is particularly effective due to their scalability and ability to handle multiple objectives, enhancing process efficiency, resource allocation, and software quality. In this paper, we present the development of a workflow software process modeled using Petri Nets. The approach helps in analyzing, controlling, and improving process execution within software development. We have proposed path testing technique applies PSO and GA to efficiently generate test paths that satisfy the all-uses criterion, improving planning and coverage in the software process. Comparative results show that PSO can achieve complete def–use path coverage with fewer generations than GA, enhancing testing efficiency and process effectiveness. The software industry increasingly emphasizes the improvement of software quality. Modern development practices recognize that software should be built and maintained through a well-defined, structured process, consisting of organized and systematic steps for designing, developing, and evolving software systems. Petri net is applied for basic block of the software process. Path-based testing supports the software process by systematically identifying key execution paths to improve test planning and coverage. Using optimization techniques like Particle Swarm Optimization (PSO) helps efficiently generate test paths, enhancing the quality and effectiveness of process-level testing activities. Search-based optimization techniques improve decision making in the software process by transforming tasks such as cost estimation, release planning, and test generation into optimization problems. These methods efficiently explore large solution spaces to find near optimal solutions. Genetic algorithm (GA) is particularly effective due to their scalability and ability to handle multiple objectives, enhancing process efficiency, resource allocation, and software quality. In this paper, we present the development of a workflow software process modeled using Petri Nets. The approach helps in analyzing, controlling, and improving process execution within software development. We have proposed path testing technique applies PSO and GA to efficiently generate test paths that satisfy the all-uses criterion, improving planning and coverage in the software process. Comparative results show that PSO can achieve complete def–use path coverage with fewer generations than GA, enhancing testing efficiency and process effectiveness.

This report presents a framework that enables the reproduction and extension of our empirical evaluations using Large Language Models (LLMs) for automated web-form-test generation. The framework includes HTML pruning , context construction , prompt design , LLM communication , and web-form-test insertion . It involves the construction of three types of prompts (from HTML) to guide the test generation: Raw HTML for Task Prompt (RH-P); LLM-Processed HTML for Task Prompt (LH-P); and Parser-Processed HTML for Task Prompt (PH-P). The framework provides an LLM communication module that standardizes interactions with provider Application Programming Interfaces (APIs). Our study utilized public-API models and demonstrated that PH-P consistently achieved a higher successfully-submitted rate (SSR) than RH-P and LH-P. To support the replication of our work, we have released the source code, a dataset subset, and the relevant scripts.

The growing complexity of educational demands and the expansion of digital learning environments have underscored the need for intelligent automation in teaching and assessment processes. Traditional educational platforms often lack adaptability, resulting in limited personalization and increased workload for educators. The integration of artificial intelligence (AI) into learning systems presents a promising avenue for addressing these challenges by enhancing scalability, efficiency, and individualized instruction. This study aimed to improve the quality and efficiency of the educational process by developing and implementing an AI-powered automated system for generating, verifying, and analyzing educational control tasks. The system was designed to support personalized learning, streamline assessment, and reduce the burden of routine academic activities. The proposed solution is built on a modular architecture using contemporary web technologies (React, Next.js, Firebase) in combination with the GPT model API. The system includes modules for test generation, automated answer checking, a conversational AI assistant, performance analytics, and real-time feedback. Document processing capabilities (DOCX, PDF) and seamless integration with Google Forms are also incorporated. The system’s performance was evaluated based on assessment accuracy, time savings, and usability. Implementation results indicate high effectiveness of the system in real educational scenarios. The automated evaluation module achieved an accuracy rate of 80–96 %, closely aligning with manual grading benchmarks. Additionally, the time required to prepare instructional content and assessments was reduced by 60–80 %. The user interface enabled intuitive access to system functionalities, and the adaptive features provided a personalized experience for students of varying proficiency levels. The developed system demonstrates significant potential for transforming educational practices through AI integration. It enhances personalization, reduces educator workload, and improves the consistency and objectivity of assessments. Future research will focus on expanding system functionality, including support for multimodal learning and large-scale institutional deployment.

Failing test generation is challenging. It involves searching in a vast space for fault-triggering test inputs and the oracles asserting these faulty executions. Despite techniques proposed to generate tests using large language models (LLMs), they are ineffective in finding failing tests, particularly for programs that implement non-trivial coding tasks such as medium/advanced-level coding contest problems. To tackle this limitation, we are inspired by an earlier finding that constituent snippets within a program typically implement simpler coding tasks compared to the program as a whole. As a result, LLMs can be leveraged to generate failing tests that target a program’s constituent snippets, thereby revealing the program defects. Leveraging this insight, we propose Mi croscopic T est Gen eration ( MitGen ), a novel technique of failing test generation. Unlike previous approaches that generate tests to fulfill code coverage, MitGen focuses on generating tests that reveal faults in a given program’s constituent code snippets. We evaluate MitGen using Starcoder2-15B-instruct-v0.1 , Meta-Llama-3-8B-Instruct and CodeQwen1.5-7B-Chat , on two popular benchmarks (EvoEval-Difficult and ClassEval) and 100 real-world subjects. We compare MitGen with three baselines, including state-of-the-art approaches (Differential Prompting and Pynguin) in finding failing tests . The evaluation results show that MitGen ’s recall is 0.66 , 112.7% enhancement over the best baseline (0.31 ).