In today’s rapidly changing software development landscape, ensuring code quality is essential to reliability, maintainability, and security among other aspects. Identifying code quality issues can be tackled; however, implementing code quality improvements can be a complex and time‐consuming task. To address this problem, we present a novel methodology designed to assist developers by suggesting alternative code snippets that not only match the functionality of the original code but also improve its quality based on predefined metrics. Our system is based on a language‐agnostic approach that allows the analysis of code snippets written in different programming languages. It employs advanced techniques to assess functional similarity and evaluates syntactic similarity, suggesting alternatives that minimize the need for extensive modification. The evaluation of our system on multiple axes demonstrates the effectiveness of our approach in providing usable code alternatives that are both functionally equivalent and syntactically similar to the original snippets, while significantly improving quality metrics. We argue that our methodology and tool can be valuable for the software engineering community, bridging the gap between the identification of code quality problems and the implementation of practical solutions that improve software quality.

Smartphones nowadays handle large amounts of sensitive user information, since users exchange undisclosed information on an everyday basis. This generates the need for more effective authentication mechanisms, deviating from the traditional ones. In this direction, many research approaches are targeted towards continuous implicit authentication, on the basis of modelling the constant interaction of the user with the device. These approaches yield promising results, however certain improvements can be made by exploiting the sequential order of the predictions and the known performance metrics. In this work, we propose a heuristics algorithm, which, given a series of predictions from any continuous implicit authentication model, can ex-ploit the sequential order in order to fix any false predictions and improve the accuracy of the smartphone security system. Preliminary evaluation on several axes indicates that our approach can effectively improve any CIA model and achieve significantly better results.

The full integration of online repositories in contemporary software development promotes remote work and collaboration. Apart from the apparent benefits, online repositories offer a deluge of data that can be utilized to monitor and improve the software development process. Towards this direction, we have designed and implemented a platform that analyzes data from GitHub in order to compute a series of metrics that quantify the contributions of project collaborators, both from a development as well as an operations (communication) perspective. We analyze contributions throughout the projects' lifecycle and track the number of coding violations, this way aspiring to identify cases of software development that need closer monitoring and (possibly) further actions to be taken. In this context, we have analyzed the 3000 most popular GitHub Java projects and provide the data to the community.

Nowadays, the continuously increasing use of smart-phones as the primary way of dealing with day-to-day tasks raises several concerns mainly focusing on privacy and security. In this context and given the known limitations and deficiencies of traditional authentication mechanisms, a lot of research efforts are targeted towards continuous implicit authentication on the basis of behavioral biometrics. In this work, we propose a methodology towards continuous implicit authentication that refrains from the limitations imposed by small-scale and/or controlled environment experiments by employing a real-world application used widely by a large number of individuals. Upon constructing our models using Support Vector Machines, we introduce a confidence-based methodology, in order to strengthen the effectiveness and the efficiency of our approach. The evaluation of our methodology on a set of diverse scenarios indicates that our approach achieves good results both in terms of efficiency and usability.