As software systems evolve, developers face a dual challenge: maintaining correctness by fixing bugs and continuously adapting functionality to meet new user demands. Traditional software engineering processes rely heavily on human developers to interpret requirements, fix errors, and ensure correctness against specifications. With the advancement of Large Language Models (LLMs) adept at code generation, the opportunity arises to shift portions of these responsibilities onto machine-driven processes. However, simply prompting an LLM to solve a complex programming task--be it eliminating a subtle bug or implementing a new feature--often falls short. Complex codebases exceed the model's context window, specification details are not always fully captured in a single prompt, and correctness requires iterative refinement guided by tests, analysis, and verification. This paper proposes a holistic, iterative framework that enables an LLM to evolve a codebase from an initial, potentially buggy state to one that satisfies not only pre-existing correctness criteria but also newly introduced feature demands. Key contributions include: 1. Unified Framework for Bug-to-Demand Resolution: We present a method by which the LLM iteratively refines code, starting from an imperfect state (with known or unknown bugs) and incrementally adjusting the codebase to meet a set of evolving functional and nonfunctional requirements introduced over time.

Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances. Whilst much work has been done proposing methods for PU learning, little has been written on the subject of evaluating these methods. Many popular standard classification metrics cannot be precisely calculated due to the absence of fully labelled data, so alternative approaches must be taken. This short commentary paper critically reviews the main PU learning evaluation approaches and the choice of predictive accuracy measures in 51 articles proposing PU classifiers and provides practical recommendations for improvements in this area.

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.