18 papers · ranked by Valyu relevance
Ronnie de Souza Santos, Maria Teresa Baldassarre, Cesar França
Quantum software testing introduces new challenges that differ fundamentally from those in classical software engineering. Aims: This study investigates how the quantum software industry defines testing roles and what skills are expected from professionals in these positions. Method: We analyzed 110 job postings from…
Ngo, Tuan-Phong, Duong, Bao-Ngoc + 6 more
Software testing is a critical component in the software engineering field and important for software engineering education. Thus, it is vital for academia to continuously improve and update educational methods to reflect the current state of the field. The International Software Testing Qualifications Board (ISTQB)…
Denesa Zyberaj, Pascal Hirmer, Marco Aiello, Stefan Wagner
The automotive domain is shifting to software-centric development to meet regulation, market pressure, and feature velocity. This shift increases embedded systems' complexity and strains testing capacity. Despite relevant standards, a coherent system-testing methodology that spans heterogeneous, legacy-constrained…
Nils Chur, Thiago Santos de Moura, Argentina Ortega, Sven Peldszus + 4 more
Robotic systems are complex and safety-critical software systems. As such, they need to be tested thoroughly. Unfortunately, robot software is intrinsically hard to test compared to traditional software, mainly since the software needs to closely interact with hardware, account for uncertainty in its operational…
Quanjun Zhang, Ye Shang, Siqi Gu, Jianyi Zhou + 3 more
Recently, the emergence of Large Language Models (LLMs) has spurred a surge of research into automated unit test generation, yielding impressive performance and reducing manual effort. However, existing LLM-based approaches still suffer from two major limitations: (1) they follow rigid, procedural workflows that…
Xiaojian Liu, Yangyang Zhang, Chee Wei Tan, Wenyi Zhang
Code coverage-guided unit test generation (CGTG) and large language model-based test generation (LLMTG) are two principal approaches for the generation of unit tests. Each of these approaches has its inherent advantages and drawbacks. Tests generated by CGTG have been shown to exhibit high code coverage and high…
Brandon Lit, Anthony Maocheia-Ricci, Thomas Driscoll
Software testing is a fundamental process of software development, and prior work has shown that visualizations of test results support testers' decision-making. However, Human-Computer Interaction research on software testing has yet to explore and understand the shared interface elements and patterns in visualization…
N. Jayalakshmi, K. Sakthivel
To achieve robust and user friendly software, it is crucial to make sure that Graphical User Interfaces (GUI) is of quality and reliable. The paper suggests a new method of Quasi-Oppositional Genetic Sparrow Search Algorithm (OOGSSA) of generating test cases efficiently in GUI. The ultimate goal is to have…
Baris Ardic, Quentin Le Dilavrec, Andy Zaidman
The integration of generative AI tools like ChatGPT into software engineering workflows opens up new opportunities to boost productivity in tasks such as unit test engineering. However, these AI-assisted workflows can also significantly alter the developer’s role, raising concerns about control, output quality, and…
S Sowmyadevi, Anna Alphy
The mutation-aware test prioritisation system in this paper uses Graph Neural Networks (GNNs) to combine static program structure, dynamic execution traces, and mutation coverage into a hybrid graph representation to enhance regression testing. The framework embeds higher-order dependencies in test cases using GCN…
Mohammadreza Namdar, Farnaz Barzinpour, Rassoul Noorossana, Mohammad Saidi-Mehrabad + 1 more
Software bug report classification is one of the most significant processes in software development for determining the nature and severity of faults based on their causes and effects. In many projects, software experts implement this process manually, which requires exorbitant time and effort. Although there are a few…
Alberto Florez Prada, Darren J. Hart
Validating bioinformatics pipelines and benchmarking sequence processing algorithms requires reliable test datasets. Existing read simulation tools rely on reference genomes and empirical error profiles, lacking fine-grained control over specific targeted DNA constructs and controlled error injection.…
Vikas Mishra, Rajkumar Verma, Paruvathanahalli Siddalingam Rajinikanth, Ravinder K. Kaundal
Quantitative analysis of rodent behaviour is fundamental to neuroscience, preclinical drug discovery and neurotoxicology research. Although several commercial and open-source software packages are available for behavioural assessment, some are expensive, some require programming expertise, and some provide limited…
Jonas Engicht, R. Maximilian Bee, Tobias Koch
This paper introduces an open-source Python package for simplified, customizable computerized adaptive testing (CAT) using Bayesian methods for ability estimation. It addresses the lack of sophisticated packages for CAT in the Python programming language. Moreover, it bridges the gap between the construction and…
Jeremy Li, Alex Rubinsteyn, Sergey Feldman, Timothy O’Donnell + 18 more
Scientific computing has become a central component of modern scientific discovery. Yet many computational tools are developed by small, specialized teams under incentives that encourage the release of rapidly prototyped tooling without commensurate attention to engineering concerns, including performance and…
Vishal Bharti, Debojyoti Chakraborty
Reproducible computational biology depends on statistical decisions that routine workflows often skip: verifying that a differential-expression test’s assumptions hold across all genes, that a strategy-comparison ANOVA is robust to non-normality, or that a meta-analysis is not distorted by publication bias. Surveys…
Carolin Schwitalla, Luis Kuhn Cuellar, Matthias Hörtenhuber, Niklas Grote + 8 more
Research software is essential for modern data analysis but is often developed and maintained by a small number of researchers. When developers leave, software may become orphaned, limiting reuse and risking the loss of valuable domain knowledge and computational methods. While the FAIR Principles for Research Software…
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The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high…