ALT Context. Despite advances in software testing and quality assurance, defects continue to recur in industrial software systems. Defect data are routinely collected in issue-tracking systems, but their use is largely confined to detection, reporting, and short-term resolution, leaving the knowledge embedded in historical defects and their fixes under-exploited for prevention and long-term improvement. Objective. This paper investigates how historical defect and defect-fix data can be analyzed, with AI assistance under human oversight, to identify the human-error origins of recurring defects and to feed that understanding back into prevention and QA improvement. Method. We developed an AI-assisted, human-in-the-loop analytics approach that classifies defects by failure type and by human-error origin across the SDLC, determines how each could have been prevented, and synthesizes engineer-focused capability-building insights. It was deployed and evaluated in a large software company through

ALT Large language models (LLMs) are increasingly used to generate Verus contracts for Rust programs together with the proofs that establish them. Yet verifier acceptance, the common measure of success, shows only that the implementation satisfies the generated contract, so it leaves open whether the contract captures the intended behavior. Assessing specification generation therefore requires an evaluation methodology, not a single metric. We present VerusEval, a framework that evaluates generated contracts along six dimensions, including Verus-native checks for semantic triviality, validated input-output (I/O) suites, and contract equivalence, and that relates them so that the strength and blind spots of each become measurable. Evaluating 13,716 artifacts from four workflows, five LLMs, and two prompting settings on 762 tasks, we find that acceptance overstates quality: only 42.3% of the accepted artifacts complete all three semantic checks without a flag, and across workflows and models

ALT Coding agents increasingly undertake development tasks in mature enterprise repositories, yet existing benchmarks offer limited insight into whether they fulfill complete Product Requirement Documents (PRDs) and where delivery breaks down. Issue resolution and development from scratch leave incremental PRD fulfillment underexplored, while isolated tests provide incomplete evidence of business and integration requirements. We introduce PACE, a benchmark of 43 enterprise tasks with 750 expert-audited Code Points. It combines requirement reconstruction, criterion construction, and evidence-based evaluation to measure PRD fulfillment and connect delivery gaps to agent behavior. Across 11 model–scaffold configurations, most scores fall between 52.1 and 78.1; GPT-5.6-Sol achieves the highest complete-delivery rate of 14.0%. Incomplete implementation and missing integration are the principal bottlenecks: producing individual components does not ensure a complete delivery. Evaluation achieves

ALT Architectural roles describe how responsibilities are distributed within software systems. Entity–Boundary–Control (EBC) separates domain information, external interaction, and workflow coordination, but its proportions across diverse software remain unclear. This study examines file-level classifier-derived EBC proportionality in 1,556,985 Java files from 732 project instances and 78 functional categories. A classifier trained on labels generated by a large language model assigned one representative role per file. The labels were aggregated at project and functional-category levels and analysed using descriptive statistics and density-based clustering. The results show that Entity was less common than Boundary and Control. The dominant cluster covered 48 categories, 472 project instances, and 47.75% of classified files; its mean composition was 13.25% Entity, 42.65% Boundary, and 44.10% Control. The remaining 30 categories form one alternative and 11 smaller specialised patterns. Some

ALT Bounded model checking unrolls a program into a single formula, so a function holding a loop costs the solver a fresh copy of that loop at every call site. Function contracts break that coupling. Verify the body once against its contract, then replace each call by a stub derived from it. CBMC supports this through dynamic frame condition checking, applied at industrial scale, but with no formal account of why the replacement is sound and no characterisation of what the frame check costs. We present the function-contract framework for ESBMC. The clause syntax, both phases, and the first assigns enforcement the tool has had are all introduced here. Its technical core is an observational frame rule: snapshot the locations the contract does not permit the function to write, then compare them at exit. The encoding emits a number of verification conditions set by how many globals the program has, not by how many writes the body performs, while dynamic frame condition checking grows with the

ALT Software engineering (SWE) benchmarks are the primary way to measure the capabilities of coding agents: an agent is placed in a code repository or machine environment, given a request, and judged by hidden tests. These benchmarks almost always start the agent from a clean state that contains no earlier work on the task. In practice, however, agents often continue work that a developer or another agent has already done and reported as complete, with no indication of what remains incorrect or missing. We introduce SWE-Residual, a benchmark for Starting-State Robustness: the ability to maintain performance on a task when the starting state already contains earlier work on that task. SWE-Residual contains 198 Residual States drawn from 111 tasks in DeepSWE and Terminal-Bench, where the earlier work modifies a code repository or a whole machine environment. Each Residual State is the state a predecessor agent left behind after submitting its work as complete, although the hidden tests still

ALT Fault localization (FL) aims to identify code locations responsible for software failures and is commonly evaluated by how accurately it ranks known fault locations. Large language model (LLM) agents are changing program repair and are increasingly used to search and reason over repositories. Although recent LLM-based FL methods have improved localization accuracy, their impact on agentic repair remains unclear. To address this gap, we conduct a systematic empirical study of FL in agentic repair. We evaluate six recent FL methods on SWE-bench Verified and Defects4J with three LLMs under a common repair framework and analyze 16,248 repair trajectories. We also examine gold locations and bug-cause descriptions as forms of FL guidance across different repair contexts. The studied FL methods do not consistently improve repair success, and higher localization accuracy does not consistently lead to larger repair gains. Yet FL guides agents' early search and helps them reach relevant code soo

ALT The construction industry increasingly relies on software built from third-party components, yet the dependency composition and known-vulnerability exposure of construction-related software remain poorly analyzed. In this study, we conduct an empirical, cross-ecosystem investigation of 904 construction-related GitHub repositories spanning six construction functions and a multifunction category. Using commit-pinned repository snapshots and OSV-Scanner, we examine dependency assessability, known-vulnerability prevalence, severity, weakness classifications, and shared upstream exposure. Our results reveal that only 494 repositories (54.6%) completed vulnerability assessment, revealing substantial gaps in dependency visibility. Among successfully scanned repositories, 395 (80.0%) contained at least one dependency version associated with a published vulnerability advisory, and 97.2% of affected repositories contained a High or Critical severity risk. Additionally, Common Weakness Enumeratio

ALT Agent pattern catalogs describe architectural alternatives, but engineers also need evidence for selecting and composing them. We extend a cognitive-function–execution-topology framework with an evidence-driven selection procedure and explicit composition contracts. The study combines two industrial case accounts, a contributor's collaboration design, seven practitioner workshops, and executable fault-injection experiments. Three local reference systems use SQLite state, shared artifact files, and a file-backed map-publication surrogate. A 2 x 2 ablation separates function-specific contracts from execution controls across 36 scenario families, with ten input variants per family. Under equal family weighting, composition correctly completes or safely refuses 11 of 12 families in each system, compared with 2, 2, and 1 for the simple baselines. These are constructed challenge-suite results, not production success rates. Recovery without consumer-side verification increases false-success c

ALT Large language models (LLMs) demonstrate strong code-generation capabilities but often struggle with functional correctness due to implicit or ambiguous behavioral requirements. We present CG2, a contract-guided code generation framework that translates natural-language tasks into structured behavioral contracts specifying interfaces, preconditions, postconditions, and invariants. CG2 fine-tunes an LLM to generate these contracts and introduces an iterative semantic validation and repair process to identify and correct incomplete, inconsistent, or incorrect specifications before guiding an unchanged downstream code-generation model. We evaluate CG2 on 1,007 held-out tasks using five code LLMs: DeepSeek-Coder-V2-Lite-Instruct, Ministral-3-14B-Instruct-2512, Qwen2.5-Coder-14B-Instruct, Qwen2.5-Coder-32B-Instruct, and Qwen3.5-27B. Results show that CG2 consistently improves pass@1 over direct code generation by 1.49-3.27 percentage points (7.58%-22.45% relative improvement) and over contr

ALT Automated generation of end-to-end web tests remains costly: developers must identify stable selectors, encode multi-step workflows, attach assertions, and maintain scripts as interfaces evolve. Recent LLM-based web agents can complete natural-language tasks in a live browser session, but task completion is not the same as producing a test-ware artifact - code that is executable, replay-able from a reset state, inspectable by developers, and tied to an oracle. We present TESSARA, an adaptive test-script generation framework that converts natural-language test intent into Playwright scripts consisting of executable interaction code, goal-state assertions, and step-level provenance linking each step to its generated code fragment, selector evidence, execution outcome, and repair history. TESSARA grounds each planned step via Step-Aligned Scripting (SAS), enabling fine-grained failure localization, selective regeneration of failing suffixes, and reversibility-aware backtracking that avoid

ALT As software agents increasingly generate and modify code, evaluating their ability to review changes and discover defects becomes important. Such evaluation needs executable project environments for investigation and reliable defect references supported by reproducible behavior. Existing code review benchmarks provide limited support for this combination. We present a system that constructs executable regression instances from historical introducing–fixing commit pairs and developer tests. The system extracts candidate tests from fixing-side changes, then uses constrained agents to transplant them and configure version-specific environments while preserving production code and test expectations. Independent replay and evidence-based review verify whether tests that fail on the introducing version and pass on the fixing version support the proposed regression relationship. Accepted instances retain both versions' replay recipes, validated tests, and execution evidence. Repair-supported

ALT Commit messages provide records of code changes and help developers understand and maintain software. To reduce the manual effort involved in writing them, commit message generation (CMG) approaches automatically generate commit messages that describe code changes in natural language. Existing CMG approaches generally treat CMG as a standalone task and optimize generated messages primarily for similarity to developer-written references. However, this objective may not properly reflect the practical usefulness of generated commit messages. This limitation matters in current AI-assisted software engineering workflows, where commit messages are used as shared information to serve different software engineering activities. To fill this gap, we propose TraceMsg, which uses issue-commit traceability, a representative software engineering activity, to guide commit message generation. TraceMsg is a collaborative multi-agent framework that coordinates message generation, traceability reasoning,

ALT Noisy Intermediate-Scale Quantum (NISQ) hardware has transformed quantum computing into a practical experimental platform while introducing fundamental challenges associated with hardware noise, decoherence, and probabilistic execution. Although quantum software testing (QST) has rapidly emerged as an active research area, it remains unclear to what extent existing testing methodologies explicitly engage with noisy and hardware-constrained execution environments. This paper presents a systematic empirical landscape analysis of noise-awareness in QST research through a deterministic, taxonomy-guided analytical framework applied to 61 papers published between 2018 and 2025 across eight testing paradigms, contrasting how contributions are framed with how they are evaluated. Among the analyzed papers, 47 (77.0%) are noise-oblivious and only 14 (23.0%) explicitly engage with noise-related execution concerns, while only 11 (18.0%) report evaluation on physical hardware or a noisy simulator.

ALT This paper introduces a Theory of Developer Momentum that helps us see the activity of developing software in motion and the skilled craft through which progress unfolds. We define Developer Momentum as the developer's experience of progress and forward motion toward the goal of a task. Our theory disentangles the related constructs of flow state, momentum, and motivation, so we can more clearly see the relationships between them and how they interweave to support progress. In order to make progress, developers build up working context in memory, become immersed in a cognitive puzzle, and gain clarity through motion in uncertain terrain. The theory provides researchers with a rich experiential lens to investigate how developers' cognitive experiences are shifting in emerging AI workflows. Building on prior grounded theory work, we conducted in-depth interviews with a theoretically sampled subset of 12 professional developers, and eight feedback interviews to evaluate resonance and inve

ALT Existing Java bug benchmarks offer limited method-level data for studying runtime-exception detection. Filtering Defects4J, Bugs.jar, Bears, and BugSwarm yields only 170 real buggy/fixed method pairs across seven unchecked exception types. We introduce RXSynth, a corpus of 4,275 synthetic Java method pairs generated from real bug/fix exemplars. We select the generator using structural alignment with real seeds and validate pairs using a two-stage patch-anchored procedure that identifies the labelled exception risk and checks whether the repair removes it. Independent JVM execution confirms patch-local validity for 84.2% of a stratified sample of 500 pairs. Provenance records enable evaluation without overlap between generation exemplars and test bugs. Structural analyses show good alignment within the represented size range, but compression of larger methods. We use RXSynth to compare isolated-method detection with pair discrimination when the repair is visible. Under provenance-contro

ALT With the emergence of specification-driven development (SDD), software modeling has regained strategic importance as a way to translate human intentions, requirements, and design decisions into precise specifications that can guide both developers and AI-assisted development tools. Despite the renewed relevance of modeling in SDD, empirical evidence remains limited on when its benefits outweigh the socio-technical effort required to create, communicate, synchronize, and maintain software models in industrial practice. This study investigates when software modeling generates value in industrial practice. We analyzed 405 survey responses collected from software practitioners. The questionnaire combined closed items concerning UML use, benefits, barriers, and improvement mechanisms with open-ended questions about current use, difficulties, and worthwhile contexts. Most respondents associated modeling with integration discussions (90 percent) and shared understanding (82 percent), but also

ALT Large Language Models (LLMs) are increasingly used to refactor source files, but file-local evaluations do not reveal changes to contracts, dependencies, or caller assumptions affecting repository adoption. To address this gap, we conduct 1,500 generation attempts across 150 production files from 15 open-source Java repositories, five LLM configurations, and two dependency-aware generation conditions: single-prompt refactoring and LangGraph-based multi-agent refactoring. Of these attempts, 1,455 yield complete candidates eligible for analysis. We evaluate these candidates using local changes, repository-facing structural indicators, detected caller impact, the behavioural-equivalence indicator, and available test evidence, and categorize them as Low-risk, Review-needed, High Contract/Dependency Risk, or Inconclusive. The results show that 1,365 candidates (93.8%) are classified as High Contract/Dependency Risk, whereas 106 (7.3%) have at least one detected broken downstream caller and

ALT Implicit dependencies, uncontrolled network access, and other build-environment impurities create intransparent software supply chains. Identifying every dependency affecting a build is a difficult task. Functional package management addresses this through reproducible packaging, ensuring software relies exclusively on declared, hermetically isolated inputs. However, creating build recipes remains complex and time-consuming, posing a significant barrier to adoption for developers and maintainers. We present Vibenix, an open-source AI-powered agent that bridges this gap by automatically generating Nix packaging expressions. Vibenix uses a large language model (LLM) to iteratively refine recipes through a rule-guided build-repair feedback loop. On 466 packaging tasks from our new NixBench dataset, Vibenix produced buildable packages in 95.3% of cases using Claude Haiku 4.5, compared with 5.6% for a state-of-the-art LLM-based single-shot packaging tool. Manual validation of 100 packages r

ALT Admission rules retain generated programs, whereas selection policies return one candidate per task. We audit how these decisions interact with execution contracts in two separately frozen, disjoint HumanEvalPlus cohorts, each comprising 32 tasks, four samples per task per profile and two served profiles. Under the inline execution contract, a structural-and-self-test screen admits 4-9 expanded-suite nonpasses per 80-107 admissions and excludes 14-43 suite passes per 128 planned attempts. A restricted module-supply condition adds 20 and 14 passing admissions for one profile in the respective cohorts, plus one follow-up nonpass, and reduces the excluded suite passes to 14-23. All fixed first-admitted outcomes and abstentions remain unchanged. In the original pool, the added passes enter already all-passing admitted pools; in the follow-up, four of 24 common sample orders lose one passed task. Selected-subset diagnostics separately address registration-sensitive completion and reference-