Generative Artificial Intelligence (genAI) is fundamentally transforming software engineering practices by shifting code production from deterministic, human-authored processes to AI-assisted development workflows characterised by probabilistic behaviour, prompt sensitivity, and increased process complexity. This transformation entails significant challenges for software quality evaluation, as traditional metrics, developed under the assumptions of determinism and reproducibility, are increasingly insufficient for assessing AI-assisted development. This position paper synthesises recent empirical and theoretical developments across three complementary dimensions of this transformation: the human dimension, examining the cognitive and educational implications of genAI adoption; the artefact dimension, analysing the evolution from code-centric development toward Everything-as-Code; and the technological dimension, focusing on emerging multi-agent software engineering workflows. The analysis examines how prompt sensitivity, output variability, governance challenges, and the expanding scope of Everything-as-Code practices complicate existing quality frameworks, and contends that evaluation methodologies must evolve toward process-aware, context-aware, cross-artifact, and workflow-level approaches. The educational implications of AI-assisted programming are also addressed, with particular emphasis on the lasting impact on professional software quality. This work provides a conceptual map of the changing software engineering landscape and identifies critical areas for further empirical investigation.
Mean coloring is an edge coloring c of a connected graph G of order 3 or more with positive integers if the chromatic mean of all vertices v of G are integers. The chromatic mean of a vertex v of G is given by cm(v)=∑ _e ∈ E_v c(e)/deg(v) where c(e) is the integers(colors) given to the edges incident to the vertex v. If each vertex has a distinct chromatic mean, then the edge coloring c is called a rainbow mean coloring. For a rainbow mean coloring c of a graph G, the maximum vertex color is the rainbow chromatic mean index, rm(c) of c. The rainbow mean index rm(G) of the graph G is defined as rm(G)=min {rm(c): c is the rainbow mean coloring} . In this paper, we determine the rainbow mean index of bistars and tadpole graphs. Also, we show that the rainbow mean index is same as the order for some derived graphs.
Large language models (LLMs) have demonstrated significant capabilities in natural language understanding and decision support. However, their deployment in critical environments requires robust mechanisms to ensure dependability and prevent erroneous inputs or outputs from compromising system dependability. This paper presents a symbolic logic reasoning approach for enhancing the validation of both user inputs and LLM outputs in critical systems. We propose a validation framework that combines the flexibility of LLMs with the logic reasoning capabilities of Answer Set Programming (ASP) as a complementary layer to existing guardrail mechanisms. The approach enables systematic validation through declarative patterns for fault tolerance and logic reasoning. Our method validates user prompts and LLM responses against external knowledge bases and domain-specific constraints using multiple strategies, including redundancy-based validation, knowledge base auditing, and consistency checking. By transforming natural language interactions into logical representations, the framework leverages ASP’s non-monotonic reasoning to detect logical inconsistencies, contradictions, knowledge reproduction errors, and constraint violations throughout the interaction pipeline. The system provides both binary decision outputs (Go/NoGo) and detailed feedback about the reasoning artifacts for auditability and explainability. We demonstrate the practical implementation through a modular architecture that supports customizable validation components.
We present the 2024 edition of the ECN AI Baseline Index (EAII), an accessible benchmark evaluating large language models in competitive programming under contest conditions. The universities division of the 2024 Sapientia–ECN (Efficiency, Challenge, Networking) programming contest included 11 teams and 16 university-track problems and introduced a judging platform restricted to C/C++/Pascal. Using a simple C++ prompting protocol with limited feedback rounds, the AI fully solved 13 of 16 problems (81.25
We study the numerical computation of nontrivial critical points of variational functionals associated with nonlinear Dirichlet problems involving the p-Laplacian. Previous numerical mountain pass approaches typically relied on finite element discretizations of the underlying function space. In contrast, we employ a discretization based on a geometric B-spline representation of the solution. The function space is approximated by smooth spline curves parameterized by control points, yielding a finite-dimensional geometric representation of the variational problem. Within this discrete space we apply a mountain-pass type up–down method. This allows the search for saddle-type critical points to be carried out directly in the space of spline control points. The descent direction is obtained through an auxiliary Poisson equation, providing a Sobolev gradient that stabilizes the iteration. Convergence of the numerical procedure is monitored via the Euler–Lagrange residual, ensuring that the computed spline approximation satisfies the variational problem up to a prescribed tolerance. Numerical experiments for the model case p=2 with nonlinearity f(u)=u^3 on Ω =(0,1) show that the method computes nontrivial solutions, including sign-changing profiles depending on the initialization. The results demonstrate the successful application of B-splines in the numerical solution of nonlinear variational problems.
