9 citations · 20 across the 31 of their papers we have counts for
33 papers
Towards LLM-Enhanced Android Taint Analysis
Nicholas Miazzo, Marco Alecci, Jordan Samhi +2
Taint analysis is a fundamental technique for detecting sensitive data leaks in Android apps. However, traditional static tools, such as FlowDroid, still face well-known challenges…
Evaluating LLMs for Obfuscation Detection and Classification in Android Apps
Luca Ferrari, Marco Alecci, Jordan Samhi +4
Android applications (apps) developers increasingly rely on code obfuscation techniques to hinder reverse engineering and protect intellectual property. However, obfuscation also r…
Neural Change Prediction: Relating Software Changes to Their Effects and Vice Versa
Laura Plein, Souhila Zidane, Jordan Samhi +1
Much of software development revolves around understanding the relationship between software changes and their effects. If we could learn and predict those relationships, such pred…
Learned or Memorized ? Quantifying Memorization Advantage in Code LLMs
Djiré Albérick Euraste, Kaboré Abdoul Kader, Jordan Samhi +3
The lack of transparency about code datasets used to train large language models (LLMs) makes it difficult to detect, evaluate, and mitigate data leakage. We present a perturbation…
From Rookie to Pro: Social Engineering LLMs for Automated Vulnerability Exploitation in Enterprise Software
Moustapha Awwalou Diouf, Maimouna Tamah Diao, Iyiola Emmanuel Olatunji +6
LLMs democratize software engineering by enabling non-programmers to create applications, but this same accessibility fundamentally undermines security assumptions that have guided…
Exploring Hidden Geographic Disparities in Android Apps
M. Alecci, P. Jiménez, J. Samhi +2
While mobile app evolution has been widely studied, geographical variation in app behavior remains largely unexplored. This paper presents a large-scale study of location-based And…
GAPS: Targeted Execution of Android Apps via Static Path Reconstruction
Samuele Doria, Alexander Pilgun, Jordan Samhi +2
Targeted execution of Android applications (apps) remains a longstanding challenge for software testing and analysis. Although recent advances in GUI testing have substantially imp…
Evaluating Large Language Models in detecting Secrets in Android Apps
Marco Alecci, Jordan Samhi, Tegawendé F. Bissyandé +1
Mobile apps often embed authentication secrets, such as API keys, tokens, and client IDs, to integrate with cloud services. However, developers often hardcode these credentials int…
SIEVE: Towards Verifiable Certification for Code-datasets
Fatou Ndiaye Mbodji, El-hacen Diallo, Jordan Samhi +3
Code agents and empirical software engineering rely on public code datasets, yet these datasets lack verifiable quality guarantees. Static 'dataset cards' inform, but they are neit…
Security Evaluation of Android apps in budget African Mobile Devices
Alioune Diallo, Anta Diop, Abdoul Kader Kabore +4
Android's open-source nature facilitates widespread smartphone accessibility, particularly in price-sensitive markets. System and vendor applications that come pre-installed on bud…
Detection of security smells in IaC scripts through semantics-aware code and language processing
Aicha War, Adnan A. Rawass, Abdoul K. Kabore +3
Infrastructure as Code (IaC) automates the provisioning and management of IT infrastructure through scripts and tools, streamlining software deployment. Prior studies have shown th…
Security smells in infrastructure as code: a taxonomy update beyond the seven sins
Aicha War, Serge L. B. Nikiema, Jordan Samhi +2
Infrastructure as Code (IaC) has become essential for modern software management, yet security flaws in IaC scripts can have severe consequences, as exemplified by the recurring ex…
Explainable AI for Analyzing Person-Specific Patterns in Facial Recognition Tasks
Paweł Jakub Borsukiewicz, Jordan Samhi, Jacques Klein +1
The proliferation of facial recognition systems presents major privacy risks, driving the need for effective countermeasures. Current adversarial techniques apply generalized metho…
How Small Transformation Expose the Weakness of Semantic Similarity Measures
Serge Lionel Nikiema, Albérick Euraste Djire, Abdoul Aziz Bonkoungou +5
This research examines how well different methods measure semantic similarity, which is important for various software engineering applications such as code search, API recommendat…
Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study
Serge Lionel Nikiema, Jordan Samhi, Micheline Bénédicte Moumoula +4
This research addresses a fundamental question in AI: whether large language models truly understand concepts or simply recognize patterns. The authors propose bidirectional reason…
MalLoc: Toward Fine-grained Android Malicious Payload Localization via LLMs
Tiezhu Sun, Marco Alecci, Aleksandr Pilgun +5
The rapid evolution of Android malware poses significant challenges to the maintenance and security of mobile applications (apps). Traditional detection techniques often struggle t…
The Code Barrier: What LLMs Actually Understand?
Serge Lionel Nikiema, Jordan Samhi, Abdoul Kader Kaboré +2
Understanding code represents a core ability needed for automating software development tasks. While foundation models like LLMs show impressive results across many software engine…
Security Assessment of Mobile Banking Apps in West African Economic and Monetary Union
Alioune Diallo, Aicha War, Moustapha Awwalou Diouf +4
The West African Economic and Monetary Union (WAEMU) states, characterized by widespread smartphone usage, have witnessed banks and financial institutions introducing mobile bankin…
Call Graph Soundness in Android Static Analysis
Jordan Samhi, René Just, Tegawendé F. Bissyandé +2
Static analysis is sound in theory, but an implementation may unsoundly fail to analyze all of a program's code. Any such omission is a serious threat to the validity of the tool's…
(In)Security of Mobile Apps in Developing Countries: A Systematic Literature Review
Alioune Diallo, Jordan Samhi, Tegawendé Bissyandé +1
In developing countries, several key sectors, including education, finance, agriculture, and healthcare, mainly deliver their services via mobile app technology on handheld devices…
AndroLog: Android Instrumentation and Code Coverage Analysis
Jordan Samhi, Andreas Zeller
Dynamic analysis has emerged as a pivotal technique for testing Android apps, enabling the detection of bugs, malicious code, and vulnerabilities. A key metric in evaluating the ef…
LLMs and Stack Overflow Discussions: Reliability, Impact, and Challenges
Leuson Da Silva, Jordan Samhi, Foutse Khomh
Since its release in November 2022, ChatGPT has shaken up Stack Overflow, the premier platform for developers queries on programming and software development. Demonstrating an abil…
Revisiting Android App Categorization
Marco Alecci, Jordan Samhi, Tegawendé F. Bissyandé +1
Numerous tools rely on automatic categorization of Android apps as part of their methodology. However, incorrect categorization can lead to inaccurate outcomes, such as a malware d…
AndroLibZoo: A Reliable Dataset of Libraries Based on Software Dependency Analysis
Jordan Samhi, Tegawendé F. Bissyandé, Jacques Klein
Android app developers extensively employ code reuse, integrating many third-party libraries into their apps. While such integration is practical for developers, it can be challeng…