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SQS: A Multi-Dimensional Framework for Evaluating Software Specifications in AI-Assisted Development

Abhavya Anekverna · Aug 15, 2026

Abstract If AI coding agents need to make a lot of inferences, they are more likely to create incorrect code, but if they have less work to do, theoretically it should be more accurate. In an AI coding agent with little to no human interaction, a specification that doesn't specify a behavior can be interpreted as a decision on implementation, with the agent continuing to work without asking for clarification. If a decision on such a behavior, constraint, interface, or acceptance condition is made, it is called a specification-induced assumption, and it is therefore an implementation decision, distinct from an ordinary implementation decision the specification leaves open (e.g., what front-end framework to use). We hypothesize that the quality of a specification impacts the correctness of implementation in the downstream, as follows: Lower quality specification = fewer assumptions an agent may have to make = less deviation from what the specification actually specifies. Current requirements-quality research has been geared towards human-centred requirements processes, and the studies of ambiguity, completeness and consistency have concentrated on them separately. This paper introduces the Specification Quality Score (SQS), a rubric-anchored, operational measure based on five dimensions of specification quality (Completeness, Unambiguity, Internal Consistency, Intent Traceability, Machine-Actionability) with clear criteria for each band of each dimension. We provide worked examples of the framework and a clear explanation of the meaning of the total score. This is a conceptual paper – it is not an empirical validation study, and does not imply that SQS predicts code generation results. This correlational statement, and the controlled experiment necessary to test it, is explicitly suggested as the next step. Keywords: requirements engineering, specification quality, large language models, AI coding agents, specification-driven development

Requirements EngineeringSoftware EngineeringNatural Language ProcessingArtificial IntelligenceComputer Science

A Mutation-Based Framework for Assessing Code Understanding Through Behavioral Change

Manish Kumar · Aug 15, 2026

A learner who can correctly predict what a piece of code will output has shown one skill; a learner who can also predict how that output changes after a small edit, and explain why, has shown something closer to real understanding. This paper builds an assessment around that second skill. Each item pairs an original program with a small, behavior-changing modification of it, evaluates both on the same fixed input, and then asks four questions in sequence: what the original program outputs, what the modified program outputs, whether the two outputs differ, and why. Conventional mutation testing uses the same kind of small, artificial change but points it at a test suite rather than a person — a mutant is "killed" if some test detects it, and the exercise measures test-suite adequacy. Here the mutant is aimed at the participant instead: it becomes the question, and the participant's response is what gets studied. The resulting instrument has seven items and a maximum score of 35 points, backed by a Python pipeline that generates the assessment, validates incoming responses, scores both the objective and free-text answers, checks the dataset for integrity problems, and produces summary statistics. No human participants have been recruited yet, so the pipeline was instead run against a clearly labeled synthetic pilot — 20 simulated respondent profiles across three ability bands, yielding 140 item-level responses. That run produced a mean score of 25.55/35 (73.0%), band means of 20.43/35, 23.29/35, and 34.17/35 for the Beginner, Intermediate, and Strong profiles, and objective-item accuracies of 80.7%, 74.3%, and 80.0% on the first three questions. None of these numbers describe real learners; they describe whether the pipeline itself behaves correctly, which it did. What this paper contributes, then, is not a set of findings but a validated instrument and pipeline, ready to be pointed at real participants. Keywords: code understanding; program comprehension; mutation analysis; mutation testing; behavioral change; programming assessment; reproducible pipeline; synthetic pilot

Mutation TestingCode Comprehensionsoftware EngineeringProgramming EducationComputer Science

Understanding chemical reactions through multimedia resolution data artistic educational tool (MRDAET).

Pokojná H et al. · Jul 1, 2026

Through the integration of science, art, and technology, we present Multimedia Resolution Data Artistic Educational Tool (MRDAET), an interactive installation designed to support the visualization and understanding of molecular reactions. The installation focuses on ATP synthesis and the electron transport chain-core biochemical processes that are often difficult to grasp due to their complexity and abstract nature. Inspired by David Goodsell's fusion of scientific accuracy and artistic expression, MRDAET employs physical 3D models derived from structural data in the Protein Data Bank, augmented with projection mapping and enhanced with illustrations. The installation encourages users to explore molecular processes through layered interactivity that combines tangible models with animated visual overlays. MRDAET was evaluated at an art and technology event, where user feedback indicated that the installation was both enjoyable and perceived as helpful in education. Participants reported improved understanding of the presented biochemical concepts and expressed interest in increased interactivity, particularly through Mixed Reality integration connecting physical models and dynamic animations. While individual and combined visual modalities have been shown to be effective in science education, immersive interactive installations of this type remain underexplored in this context. This paper presents MRDAET as the third iteration following prior designs, offering a science-driven educational approach that warrants further study and application to additional scientific areas.

Computer Science

Patient Perceptions and Acceptance of Blockchain-Based Health Data Sharing in Oncology: Cross-Sectional Survey.

Moraes MV et al. · Jun 25, 2026

Background Fragmentation of electronic health records in oncology hinders coordinated care, delays diagnoses, and limits therapeutic personalization. Blockchains promise to promote secure, interoperable, and patient-centered data governance; however, patient perceptions of blockchains remain underexplored, particularly in middle-income countries such as Brazil. Objective We assessed opinions, attitudes, and willingness among patients with cancer to digitally share clinical information and the feasibility of applying blockchains to restructuring secure health data sharing in the Brazilian public health context. We had three research questions: (1) What is the level of digital health tool acceptance among patients with cancer in Brazil? (2) Which sociodemographic factors are associated with willingness to share health data? (3) Are blockchains feasible and acceptable for restructuring secure oncology data sharing? Methods An exploratory, descriptive, cross-sectional self-report survey was conducted at Hospital Santa Izabel, a national oncology reference center in Salvador, Bahia, Brazil, between September and November 2023. A convenience sample of 110 outpatients with cancer was recruited systematically; data were collected via a self-administered questionnaire. The 20-item instrument, developed de novo and validated via expert panel and pilot testing, covered 5 content domains yielding 3 composite scoring domains: self-management, adherence, and governance. We used Cronbach α to assess internal consistency, independent 2-tailed t tests, 1-way ANOVA, and Pearson correlations to compare domain scores across sociodemographic groups, and a chi-square goodness-of-fit test to examine trust proportions across recipient types. Results We received sufficiently complete responses from 94.5% (104/110) of patients. The sample was predominantly female (63/98, 64.3%), self-identified as pardo (mixed-race; 64/98, 65.3%), and lower income (55/96, 57.3% earned less than twice the minimum wage). Acceptance of technology was high: 86.4% (95/110) would use health apps and 89.1% (98/110) expressed interest in prevention-focused applications. Trust in data sharing varied significantly across recipient types (χ23=210.4; P Conclusions Patients with cancer in Brazil demonstrated high acceptance of digital health tools and openness to data sharing when privacy, security, and governance are guaranteed. This supports the feasibility of blockchain-based health data management systems, provided they incorporate patient-centered principles, digital inclusion strategies, and robust governance aligned with Brazilian regulations (the General Data Protection Law) and the Unified Health System (Sistema Único de Saúde) infrastructure. Importantly, patient support reflected acceptance of blockchain's functional principles, data security, anonymization, and auditability rather than familiarity with the technology itself, a distinction with direct implications for future implementation studies.

Computer Science