21 papers · ranked by Valyu relevance
Priyank Desai, Snahil Singh, Shubham Amilkanthwar
Ensuring robust test coverage, high code quality, and a strong security posture are persistent challenges in modern industrial software development, especially as systems grow in complexity and release cycles accelerate with recent Artificial Intelligence (AI) related productivity gains. This paper introduces a…
Sean Lowe, Elias Hilaneh, Alma Babbit, Nakul Gopalan + 2 more
Hardware verification is one of the most challenging stages of the hardware design process, requiring significant time and resources to ensure a design is fully validated and production-ready. Verification teams aim to maximize design coverage while ensuring correct behavior and alignment with the specification.…
Rachna Raj, Diego Elias Costa
Open-source software (OSS) is a pillar of modern software development. Its success depends on the dedication of maintainers who work constantly to keep their libraries stable, adapt to changing needs, and support a growing community. Yet, they receive little to no continuous feedback on how the projects that rely on…
Atish Kumar Dipongkor, Talank Baral, Wing Lam, Kevin Moran
AI coding agents increasingly submit complete pull requests (PRs) with minimal human intervention, shifting software development from AI-assisted to autonomous workflows. As these agents become more prevalent, ensuring the code they generate is adequately tested, by existing tests or by tests the agents write, is…
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…
Florian Tambon, Michael Konstantinou, Cedric Richter, Charles Chenouard + 2 more
In recent years, it has become increasingly evident that large language models (LLMs) and autonomous agents raise the level of abstraction in software development by shifting the focus from writing precise procedures to expressing intents and goals. This paradigm shift introduces new challenges, particularly in how…
Yao-zhong Zhang, Satoshi Hashimoto, Sihan Li, Toshifumi Inada + 1 more
Ribosome profiling (Ribo-seq) provides codon-resolution measurements of translation; however, many transcripts exhibit sparse or low read coverage, which limits downstream quantitative analyses. Reliable prediction and imputation of codon-resolution coverage for low-coverage transcripts remain computationally…
Partha Protim Paul, Reid Holmes
Traditional test adequacy metrics measure a system's implementation, not whether it adheres to its expected behaviour. While developers rely heavily on code coverage and mutation testing to assess test suite quality, these metrics are fundamentally implementation-centric and cannot detect gaps between what the code is…
Shiyao Chen, Urša Zevnik, Christoph Ziegenhain
Gene-body coverage bias differs across scRNA-seq protocols and can influence downstream analyses, yet coverage is often assessed using bulk-level summaries that obscure cell-to-cell variability. CellCov provides gene-body coverage profiling at single-cell resolution, enabling exploration of coverage heterogeneity…
Diany Pressato, Honghao Tan, Mariam Elmoazen, Shin Hwei Tan
Developers are increasingly overwhelmed by AI-generated issue reports that lack actionability and reproducibility, eroding trust in automated bug detection tools. In this paper, we present IssueSpecter, an automated tool that finds bugs in uncovered code segments and automatically generates prioritized, actionable…
Tom Pollard, Thomas Sounack, Catherine A. Gao, Leo Anthony Celi + 5 more
Introduction The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement was published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to…
Célia Biane, Kyungduk Moon, Kangbok Lee, Loïc Paulevé
Boolean networks are discrete dynamical models that use Boolean states and logical functions to represent the dynamics of biological systems. A primary application of Boolean networks is to identify controls (e.g., genetic mutations or knockouts) that drive the system toward a desired phenotype. However, existing…
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…
Youngseok Lee, Jie Cao, Gaofei Sun
Phosphene-based visual neuroprostheses require encoding schemes that preserve task-relevant information while remaining feasible under safety-constrained stimulation. This study proposes a stress-based evaluation framework that reframes phosphene encoder assessment as a tri-objective operating-envelope problem rather…
Authors not listed
Graph Neural Networks (GNNs) are powerful tools for molecular property prediction, but they are not magic. When applied to molecules unlike their training data, they produce unreliable predictions that are difficult to detect. The Applicability Domain (AD) concept addresses this by defining regions of chemical space…
Tongcheng Geng, Muhammad Ahsan
Deep code models face security vulnerabilities through backdoor attacks. Previous approaches have primarily relied on single-trigger mechanisms, resulting in limited stealth and vulnerability to defense strategies. This paper proposes a novel hybrid backdoor attack method that combines function signature features and…
Hao Li, Yifei Lu, Kaiwen Fang, Zixi Xu + 1 more
AI-scientist systems are beginning to automate parts of scientific research. We present ContinuumCellAgent, an autonomous agent that executes literature review, hypothesis formation, computational experimentation, manuscript drafting, and adversarial peer review as a single unattended run. Existing AI scientist systems…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Shreyansh Agrawal, Harsh B. Anadkat, Kiran K. Athimoolam, Harsh Bhardwaj + 8 more
Recent advances in artificial intelligence (AI) have prompted claims about autonomous “AI scientists,” yet systematic evaluations of these capabilities remain scarce. This exploratory study investigates whether current AI frameworks can execute scientific research tasks beyond isolated demonstrations. We tested eight…
Authors not listed
Protein kinases play a crucial role in key regulatory cell processes and are known to be dysregulated in diseases such as cancer and autoimmune disorders. Hence, protein kinases represent a vital drug target class. To meet the challenge of designing novel kinase inhibitors, fragment-based drug discovery (FBDD) has…
Authors not listed
Reactions that proceed via dissociative mechanisms have competing requirements, namely empty sites for the dissociation step and high coverage of species for the subsequent product formation. This competition reduces activity and selectivity under steady-state conditions and allows for improved catalytic activity by…