Publications (274)
ARTICLE
Software Engineering for OpenHarmony: A Research Roadmap
Li Li, Xiang Gao, Hailong Sun, Chunming Hu, Carolyn Sun, Haoyu Wang, Haipeng Cai, Ting Su, Xiapu Luo, Tegawendé Bissyande, Jacques Klein, John Grundy, Tao Xie, Haibo Chen, Huaimin Wang
Mobile software engineering has been a hot research topic for decades. Our fellow researchers have proposed various approaches (with over 7,000 publications for Android alone) in this field that essentially contributed to the great success of the current mobile ecosystem. Existing research efforts mainly focus on popular mobile platforms, name(...)
Software Engineering
ARTICLE
Health centers network analysis with Gephi and ForceAtlas2 approach: Case of Burkina Faso
Saan-nonnan Olivier Dabire, Désiré Guel, Boureima Zerbo
Burkina Faso, like many developing countries, faces significant challenges in public health, particularly regarding healthcare access and infrastructure distribution. Healthcare centers are unevenly distributed across regions, resulting in disparities in access to care. This study aims to analyze the structure and efficiency of the healthcare(...)
Force Atlas2, Graph theory, Modularity, Density, Health network, Burkina Faso
COMMUNICATION
Do you have 5 min? Improving Call Graph Analysis with Runtime Information
Jordan Samhi, Marc Miltenberger, Marco Alecci, Steven Arzt, Tegawendé Bissyandé, Jacques Klein
Constructing precise and sound call graphs is fundamental for effective static analysis, yet it remains a significant challenge in today's software. Traditionally, researchers have developed sophisticated algorithms to address this issue, often resulting in increased computational costs. But what if we could provide a simple, cost-effective wa(...)
Improving Call Graph
ARTICLE
Artificial intelligence for IoT threat detection: Case of DDoS attacks
Dr. Yacouba OUATTARA Dr. Yaya TRAORE D. Moumine Arthur OUEDRAOGO
With the rapid expansion of connected objects in our daily lives, the risks of cyberattacks, particularly by distributed denial of service (DDoS), have increased considerably. IoT devices, often designed with few resources and little protection, are easy targets for cybercriminals. In this context, our study explores the role of artificial int(...)
IoT, DDoS, Machine Learning, Deep Learning, K-Means, Isolation Forest, Random Forest
ARTICLE
DDoS Attacks Simulation on a Storage Cloud: Impacts and Appropriate Security Mechanisms
Almissi Amed Kindo, Didier Bassole, Gouayon Koala, Oumarou Sié
Safety, availability, confidentiality and integrity are essentials characteristics for any user of cloud services. Distributed Denial of Service (DDoS) attacks are one of the most widespread threats on cloud computing. In this paper, we simulate DDoS attacks on a storage cloud and we try to assess the impact on the integrity, availability and(...)
DDoS, Cloud Computing, Storage cloud, LOIC, XerXes
ARTICLE
ChatGPT Does Not Understand Moore
Aminata Sabané, Tegawendé F. Bissyandé
The advent of Large Language Models (LLMs) likely constitutes a turning point in the history of mankind. LLMs have the potential to revolutionize the way we interact with computers and each other. While current state of the art LLMs mostly support western languages, such as English, it is paramount that we consider the opportunity of integrati(...)
Computer science
ARTICLE
You Don’t Have to Say Where to Edit! jLED—Joint Learning to Localize and Edit Source Code
Weiguo Pian, Yinghua Li, Haoye Tian, Tiezhu Sun, Yewei Song, Xunzhu Tang, Andrew Habib, Jacques Klein, Tegawendé F. Bissyandé
Learning to edit code automatically is becoming more and more feasible. Thanks to recent advances in Neural Machine Translation (NMT), various case studies are being investigated where patches are automatically produced and assessed either automatically (using test suites) or by developers themselves. An appealing setting remains when the deve(...)
Learning
ARTICLE
Advancements in Deep Learning for Malaria Detection: A Comprehensive Overview
Kiswendsida Kisito Kabore and Desire Guel and Flavien Herve Somda
Malaria remains a critical global health issue, with millions of cases reported annually, particularly in resource-limited regions. Timely and accurate diagnosis is vital to ensure effective treatment, reduce complications, and control transmission. Conventional diagnostic methods, including microscopy and Rapid Diagnostic Tests (RDTs), face c(...)
Malaria detection, Deep Learning, Convolutional Neural Networks (CNN), Medical Imaging, Automated diagnostics
ARTICLE
AudioTest: Prioritizing Audio Test Cases
Yinghua Li, Xueqi Dang, Wendkûuni C. Ouédraogo, Jacques Klein, Tegawendé F. Bissyandé
Audio classification systems, powered by deep neural networks (DNNs), are integral to various applications that impact daily lives, like voice-activated assistants. Ensuring the accuracy of these systems is crucial since inaccuracies can lead to significant security issues and user mistrust. However, testing audio classifiers presents a signif(...)
Prioritizing Audio Test Cases
ARTICLE
The Struggles of LLMs in Cross-Lingual Code Clone Detection
Micheline Bénédicte Moumoula, Abdoul Kader Kaboré, Jacques Klein, Tegawendé F. Bissyandé
With the involvement of multiple programming languages in modern software development, cross-lingual code clone detection has gained traction within the software engineering community. Numerous studies have explored this topic, proposing various promising approaches. Inspired by the significant advances in machine learning in recent years, par(...)
LLMs
ARTICLE
Assessing Robustness and Resistance to Attacks of an Authentication System Based on OpenID Connect Protocol and Ethereum Blockchain
Ilboudo Assane; Bassole Didier; Kouraogo Justin Pegdwindé; Koala Gouayon; Sie Oumarou
In this paper, we assess the robustness and resistance against various types of attacks of a multi-factor authentication mechanism that we have proposed. It is a mechanism based on the OpenID Connect protocol and utilizes the Ethereum blockchain. Robustness was evaluated by conducting appropriate security tests using AVISPA and Scyther protoco(...)
Single Sign-On, Robustness, Ethereum Blockchain, OpenID Connect
ARTICLE
Vulnerabilities in infrastructure as code: what, how many, and who?
Aicha War, Alioune Diallo, Andrew Habib, Jacques Klein, Tegawendé F. Bissyandé
Infrastructure as Code (IaC) is a pivotal approach for deploying and managing IT systems and services using scripts, offering flexibility and numerous benefits. However, the presence of security flaws in IaC scripts can have severe consequences, as exemplified by the recurring exploits of Cloud Web Services. Recent studies in the literature ha(...)
Vulnerabilities
ARTICLE
Détection de communautés dans les graphes de connaissance d'activités
Marthe Désirée Olivia HABACK , Serge SONFACK SOUNCHIO , Orlane SONKENG TSAFACK , Halguièta NASSA TRAWINA , Ho Tuong Vinh
La gestion des connaissances produites au cours des activités au sein des organisations joue un rôle important dans leur développement et leur succès. La formalisation de ces connaissances sous forme de graphe de connaissance d’activités (Activity Knowledge Graph : AKG) permet de représenter et de réutiliser ces connaissances pour résoudre des(...)
Graphe de connaissance, graphe de connaissance d’activité, détection de communauté, algorithme de Louvain
ARTICLE
How Are We Detecting Inconsistent Method Names? An Empirical Study from Code Review Perspective
Kisub Kim, Xin Zhou, Dongsun Kim, Julia Lawall, Kui Liu, Tegawende F. Bissyande, Jacques Klein, Jaekwon Lee, David Lo
Proper naming of methods can make program code easier to understand, and thus enhance software maintainability. Yet, developers may use inconsistent names due to poor communication or a lack of familiarity with conventions within the software development lifecycle. To address this issue, much research effort has been invested into building aut(...)
Inconsistent
ARTICLE
LLM for Mobile: An Initial Roadmap
Daihang Chen, Yonghui Liu, Mingyi Zhou, Yanjie Zhao, Haoyu Wang, Shuai Wang, Xiao Chen, Tegawendé F. Bissyandé, Jacques Klein, Li Li
When mobile meets LLMs, mobile app users deserve to have more intelligent usage experiences. For this to happen, we argue that there is a strong need to apply LLMs for the mobile ecosystem. We therefore provide a research roadmap for guiding our fellow researchers to achieve that as a whole. In this roadmap, we sum up six directions that we be(...)
LLM