arXiv subjects

Imen Trabelsi

Publications and source records attributed to Imen Trabelsi.

5 recordsLinked to original sources

arXiv2026

GitHub Template Repositories: Served Domains, Maintenance, and Practitioner Guidelines

Over time, GitHub has introduced different strategies for sharing reusable code artifacts. In addition to fork-based reuse, template repositories provide a distinct feature for generating new projects from scaffolding. Although this feature has been available since 2019, little is known about the domains it supports, its maintenance characteristics, or the practices that guide practitioners for effective template design. To address this gap, we conduct a large-scale empirical study of GitHub template repositories across the five most used programming languages. First, we mine and categorize templates to analyze the domains they serve, exploring the LLM-as-a-judge strategy. Next, we explore the reliability of templates by evaluating the associations between repository characteristics and activity, and quality-related issues (e.g., code smells, vulnerabilities, and security hotspots) through statistical analysis. Finally, we qualitatively analyze a representative subset of templates to derive practical guidelines and recurring pitfalls for template design and management. Our results show that Web Development is the predominant domain across ecosystems, while maintenance and quality issues vary by programming language. We further find that high-quality templates tend to adopt established software engineering practices, while providing comprehensive documentation and clear guidance for use. Overall, our findings offer empirical insights and actionable guidance to support practitioners in designing and adopting high-quality template repositories.

cs.SE↗

arXiv2025

A Systematic Literature Review of Machine Learning Approaches for Migrating Monolithic Systems to Microservices

Scalability and maintainability challenges in monolithic systems have led to the adoption of microservices, which divide systems into smaller, independent services. However, migrating existing monolithic systems to microservices is a complex and resource-intensive task, which can benefit from machine learning (ML) to automate some of its phases. Choosing the right ML approach for migration remains challenging for practitioners. Previous works studied separately the objectives, artifacts, techniques, tools, and benefits and challenges of migrating monolithic systems to microservices. No work has yet investigated systematically existing ML approaches for this migration to understand the \revised{automated migration phases}, inputs used, ML techniques applied, evaluation processes followed, and challenges encountered. We present a systematic literature review (SLR) that aggregates, synthesises, and discusses the approaches and results of 81 primary studies (PSs) published between 2015 and 2024. We followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement to report our findings and answer our research questions (RQs). We extract and analyse data from these PSs to answer our RQs. We synthesise the findings in the form of a classification that shows the usage of ML techniques in migrating monolithic systems to microservices. The findings reveal that some phases of the migration process, such as monitoring and service identification, are well-studied, while others, like packaging microservices, remain unexplored. Additionally, the findings highlight key challenges, including limited data availability, scalability and complexity constraints, insufficient tool support, and the absence of standardized benchmarking, emphasizing the need for more holistic solutions.

cs.SE↗

arXiv2014

On the Use of Different Feature Extraction Methods for Linear and Non Linear kernels

The speech feature extraction has been a key focus in robust speech recognition research; it significantly affects the recognition performance. In this paper, we first study a set of different features extraction methods such as linear predictive coding (LPC), mel frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) with several features normalization techniques like rasta filtering and cepstral mean subtraction (CMS). Based on this, a comparative evaluation of these features is performed on the task of text independent speaker identification using a combination between gaussian mixture models (GMM) and linear and non-linear kernels based on support vector machine (SVM).

cs.CL↗

arXiv2014

A Multi Level Data Fusion Approach for Speaker Identification on Telephone Speech

Several speaker identification systems are giving good performance with clean speech but are affected by the degradations introduced by noisy audio conditions. To deal with this problem, we investigate the use of complementary information at different levels for computing a combined match score for the unknown speaker. In this work, we observe the effect of two supervised machine learning approaches including support vectors machines (SVM) and naïve bayes (NB). We define two feature vector sets based on mel frequency cepstral coefficients (MFCC) and relative spectral perceptual linear predictive coefficients (RASTA-PLP). Each feature is modeled using the Gaussian Mixture Model (GMM). Several ways of combining these information sources give significant improvements in a text-independent speaker identification task using a very large telephone degraded NTIMIT database.

cs.SD↗

arXiv2014

Improved Frame Level Features and SVM Supervectors Approach for the Recogniton of Emotional States from Speech: Application to categorical and dimensional states

The purpose of speech emotion recognition system is to classify speakers utterances into different emotional states such as disgust, boredom, sadness, neutral and happiness. Speech features that are commonly used in speech emotion recognition rely on global utterance level prosodic features. In our work, we evaluate the impact of frame level feature extraction. The speech samples are from Berlin emotional database and the features extracted from these utterances are energy, different variant of mel frequency cepstrum coefficients, velocity and acceleration features.

cs.CL↗