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20242026
most cited(In)Security of Mobile Apps in Developing Countries: A Systematic Literature Review

2 citations · 8 across the 19 of their papers we have counts for

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Showing 2025 · cs.SEShow all

6 papers · 2 filters

cs.SE2025

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…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2025

Multi-Agent Strategies for Bridging Programming Language Gaps in Code Generation

Micheline Bénédicte Moumoula, Serge Lionel Nikiema, Albérick Euraste Djire +3

Real-world software systems are inherently multilingual, but current large language models are not equally consistent across programming languages. This mismatch limits code genera…

cs.SE2025★ 2 cited

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…