feasibility assessment

The practical feasibility of existing Sustainable Software Engineering (SSE) tools in regulated industries, such as finance, remains unclear. This study addresses this gap through a mixed-methods approach—combining an interactive web application taxonomy, qualitative workshops, and quantitative surveys—to systematically uncover developer preferences regarding ease of installation, minimal input requirements, and actionable outputs, including IDE/CI integration, dashboards, and automated refactoring suggestions. It further identifies key technical, organizational, and compliance-related factors influencing tool adoption. The findings provide empirical grounding for designing SSE tools tailored to regulated environments, highlighting how approval workflows and stringent compliance demands significantly constrain tool viability.

Software engineering research has long lacked a structured methodology to guide researchers in formulating industrially relevant research questions. Method: This paper proposes and empirically validates a seven-dimensional problem modeling framework—comprising Actual Problem, Context, Impact, Practitioners, Evidence, Goal, and Research Question—and innovatively incorporates financial dimensions (e.g., ROI) and feasibility constraints to enhance industrial applicability. We conducted an empirical evaluation with 42 senior SE researchers via participatory workshops using Problem Vision boards, structured questionnaires, and qualitative analysis. Contribution/Results: The framework significantly improves the practical relevance and operationalizability of research questions. It yields actionable guidelines for refining problem formulation, thereby effectively bridging the gap between academic research and industrial needs.

Existing task planning approaches suffer from limited efficiency and interpretability in detecting goal unreachability, explaining infeasibility causes, and adapting to dynamic changes in goals or constraints. This work proposes a planning framework based on relaxed Petri net reachability, integrating incremental constraint solving with invariant synthesis to enable efficient detection of infeasible plans and provide interpretable feedback, while supporting dynamic updates in sequential task planning. Experimental results demonstrate that the proposed method identifies up to twice as many infeasible scenarios as baseline approaches, generates a comparable number of invariants, matches baseline performance in single-shot planning, and significantly outperforms existing methods in sequential planning scenarios involving dynamic updates.

In prediction-to-optimization (PtO) frameworks for constrained optimization problems (COPs), parameter uncertainty often leads to infeasible decisions. To address this, we propose Decision-Forward Learning (DFL), a novel framework that jointly optimizes decision quality and feasibility. Unlike existing DFL approaches—which neglect the feasibility of predicted constraint parameters—our method is the first to explicitly model and optimize the feasibility of predicted parameters within the DFL paradigm. We design two maximum-likelihood-based loss functions, jointly penalizing suboptimality and constraint violation, with a tunable trade-off weight. The framework supports general COPs, including nonlinear and mixed-integer formulations. Experiments across multiple benchmark instances demonstrate that our approach achieves an effective balance between feasibility and decision quality, matching or exceeding the performance of state-of-the-art baselines.

This work addresses the critical problem of unexplainable unrealizability verification in program synthesis. We propose the first Hoare-style unrealizability logic—a formal deductive system that models the semantic behavior of program search spaces and systematically approximates the collective execution behavior of infinite program sets via sound, machine-checkable inference rules. This transforms opaque, black-box unrealizability proofs into human-understandable, machine-verifiable structured derivations. Our key contributions are threefold: (1) unifying and formalizing the implicit reasoning principles underlying existing unrealizability tools; (2) enabling inductive assertion synthesis and rigorous formal verification of unrealizability; and (3) yielding a compositional, extensible proof infrastructure. The logic enhances transparency, theoretical rigor, and tool-supported verifiability in program synthesis analysis—establishing a foundational framework for principled, explainable synthesis correctness reasoning.

This work addresses the lack of interpretable feedback in industrial automation capability planning under infeasible conditions and the difficulty in adapting to dynamic operational environments. The paper proposes the first hybrid decision-support system that integrates SMT-based symbolic planning with large language models (LLMs), enabling natural-language explanations of planning outcomes and user-authorized, adaptive updates to the knowledge model. Leveraging a human-in-the-loop mechanism and a routed multi-agent workflow, the system successfully completed all four feasible planning tasks and nine out of ten knowledge queries across 23 test cases. For three out of four infeasible scenarios, it generated actionable repair suggestions, and in five adaptation scenarios, it achieved feasible plans through user-approved knowledge modifications—establishing the first capability planning framework that is interpretable, interactive, iterative, and formally correct.

This study addresses the feasibility determination problem under subjective probability constraints within a finite set of alternative systems. The authors propose a statistical inference method that operates directly on Bernoulli simulation outputs, uniquely integrating multi-threshold subjective constraints with Bernoulli observations without relying on normal approximations. To handle extreme scenarios—such as when all systems are feasible or none are—the method incorporates two heuristic strategies that dynamically adjust thresholds during execution. The resulting batch-mean-independent testing algorithm maintains rigorous statistical validity while significantly outperforming existing approaches designed for normally distributed data. Empirical experiments demonstrate the method’s computational efficiency and robust adaptability across diverse problem settings.

Existing tool-augmented agents struggle to recognize infeasible tasks, often leading to futile reasoning and wasted computational resources. To address this limitation, this work proposes FeasiGen, a novel approach that leverages multi-agent collaboration to extract critical tools from successful trajectories, automatically generates infeasible task instances via masking, and constructs a high-quality dataset validated by human annotators with over 94% accuracy. The study further establishes the first feasibility-aware evaluation framework, revealing that current models erroneously persist in executing 73.9% of infeasible tasks. In contrast, the proposed multi-agent architecture substantially reduces this error rate, demonstrating its superior capability in discerning task feasibility.

This work addresses the lack of formal guarantees regarding semantic preservation during problem reformulation and solver correctness in constraint programming. It presents the first end-to-end verified framework implemented in the Lean theorem prover, enabling formal proofs of parameterized equivalence, equisatisfiability, and symmetry-breaking correctness for entire families of problems. The approach combines general, parameterized proofs with instance-level certificate checking, thereby eliminating the need to trust external solvers. Verified certificates are produced via backend transformations, and a single high-level proof suffices for arbitrarily large instances. This methodology achieves dramatic search-space reductions—up to a factor of twenty million—and enables full verification of the largest instances in just a few minutes.

This study addresses the problem of extracting an optimal subplan from an existing plan under a budget constraint, while preserving the original actions and their execution order. The goal is to identify a subplan that respects a given cost upper bound, remains executable, and maximizes utility. The decision variant of this problem is proven to be NP-complete. To tackle it, the authors propose a refined integer linear programming (ILP) formulation that significantly reduces model size and enhances computational efficiency without sacrificing solution accuracy. Together with over-subscription planning (OSP), this ILP approach constitutes one of two exact solution methods. Compared to prior work, the proposed ILP method demonstrates marked improvements in both scalability and empirical performance.