Publications (125)
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode
Nadia Daoudi, Jordan Samhi, Abdoul Kader Kabore +3
Computer vision has witnessed several advances in recent years, with unprecedented performance provided by deep representation learning research. Image formats thus appear attracti…
IBIR: Bug Report driven Fault Injection
Ahmed Khanfir, Anil Koyuncu, Mike Papadakis +4
Much research on software engineering and software testing relies on experimental studies based on fault injection. Fault injection, however, is not often relevant to emulate real-…
Static Analysis for Extracting Permission Checks of a Large Scale Framework: The Challenges And Solutions for Analyzing Android
Alexandre Bartel, Jacques Klein, Martin Monperrus +1
A common security architecture is based on the protection of certain resources by permission checks (used e.g., in Android and Blackberry). It has some limitations, for instance, w…
Early Detection of Security-Relevant Bug Reports using Machine Learning: How Far Are We?
Arthur D. Sawadogo, Quentin Guimard, Tegawendé F. Bissyandé +3
Bug reports are common artefacts in software development. They serve as the main channel for users to communicate to developers information about the issues that they encounter whe…
Human-Aligned Code Readability Assessment with Large Language Models
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +6
Code readability is crucial for software comprehension and maintenance, yet difficult to assess at scale. Traditional static metrics often fail to capture the subjective, context-s…
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…
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…
MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning
Weiguo Pian, Hanyu Peng, Xunzhu Tang +5
Representation learning of source code is essential for applying machine learning to software engineering tasks. Learning code representation from a multilingual source code datase…
CallNavi, A Challenge and Empirical Study on LLM Function Calling and Routing
Yewei Song, Xunzhu Tang, Cedric Lothritz +6
API-driven chatbot systems are increasingly integral to software engineering applications, yet their effectiveness hinges on accurately generating and executing API calls. This is…
JuCify: A Step Towards Android Code Unification for Enhanced Static Analysis
Jordan Samhi, Jun Gao, Nadia Daoudi +6
Native code is now commonplace within Android app packages where it co-exists and interacts with Dex bytecode through the Java Native Interface to deliver rich app functionalities.…
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…
LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings
Fred Philippy, Siwen Guo, Jacques Klein +1
Sentence embedding models play a key role in various Natural Language Processing tasks, such as in Topic Modeling, Document Clustering and Recommendation Systems. However, these mo…
I know what leaked in your pocket: uncovering privacy leaks on Android Apps with Static Taint Analysis
Li Li, Alexandre Bartel, Jacques Klein +6
Android applications may leak privacy data carelessly or maliciously. In this work we perform inter-component data-flow analysis to detect privacy leaks between components of Andro…
Model-Driven Analytics: Connecting Data, Domain Knowledge, and Learning
Thomas Hartmann, Assaad Moawad, Francois Fouquet +4
Gaining profound insights from collected data of today's application domains like IoT, cyber-physical systems, health care, or the financial sector is business-critical and can cre…
Enhancing Small Language Models for Cross-Lingual Generalized Zero-Shot Classification with Soft Prompt Tuning
Fred Philippy, Siwen Guo, Cedric Lothritz +2
In NLP, Zero-Shot Classification (ZSC) has become essential for enabling models to classify text into categories unseen during training, particularly in low-resource languages and…
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…
Semantic Drift in Bug Resolution: How Behavioral Signals Propagate from Reports to Tests and Patches
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +7
Bug resolution is a cross-artifact process: natural-language reports must expose actionable behavioral cues that can be reproduced by tests and corrected by patches. Yet the extent…
TriggerZoo: A Dataset of Android Applications Automatically Infected with Logic Bombs
Jordan Samhi, Tegawendé F. Bissyandé, Jacques Klein
Many Android apps analyzers rely, among other techniques, on dynamic analysis to monitor their runtime behavior and detect potential security threats. However, malicious developers…
FraudDroid: Automated Ad Fraud Detection for Android Apps
Feng Dong, Haoyu Wang, Li Li +5
Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a s…
From Rookie to Expert: Manipulating 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…
A First Look at Android Applications in Google Play related to Covid-19
Jordan Samhi, Kevin Allix, Tegawendé F. Bissyandé +1
Due to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the Covid-19 pa…
Dexpler: Converting Android Dalvik Bytecode to Jimple for Static Analysis with Soot
Alexandre Bartel, Jacques Klein, Martin Monperrus +1
This paper introduces Dexpler, a software package which converts Dalvik bytecode to Jimple. Dexpler is built on top of Dedexer and Soot. As Jimple is Soot's main internal rep- rese…
How are We Detecting Inconsistent Method Names? An Empirical Study from Code Review Perspective
Kisub Kim, Xin Zhou, Dongsun Kim +6
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…
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…
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
Haoye Tian, Yinghua Li, Weiguo Pian +5
Towards predicting patch correctness in APR, we propose a simple, but novel hypothesis on how the link between the patch behaviour and failing test specifications can be drawn: sim…
Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair
Haoye Tian, Kui Liu, Abdoul Kader Kaboreé +4
A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an o…
RAICC: Revealing Atypical Inter-Component Communication in Android Apps
Jordan Samhi, Alexandre Bartel, Tegawendé F. Bissyandé +1
Inter-Component Communication (ICC) is a key mechanism in Android. It enables developers to compose rich functionalities and explore reuse within and across apps. Unfortunately, as…
An Extensive Systematic Review on Model-Driven Development of Secure Systems
Phu H. Nguyen, Max Kramer, Jacques Klein +1
Context: Model-Driven Security (MDS) is as a specialised Model-Driven Engineering research area for supporting the development of secure systems. Over a decade of research on MDS h…
Enhancing Text-to-SQL Translation for Financial System Design
Yewei Song, Saad Ezzini, Xunzhu Tang +6
Text-to-SQL, the task of translating natural language questions into SQL queries, is part of various business processes. Its automation, which is an emerging challenge, will empowe…
LuxInstruct: A Cross-Lingual Instruction Tuning Dataset For Luxembourgish
Fred Philippy, Laura Bernardy, Siwen Guo +2
Instruction tuning has become a key technique for enhancing the performance of large language models, enabling them to better follow human prompts. However, low-resource languages…
DreamFactory: Pioneering Multi-Scene Long Video Generation with a Multi-Agent Framework
Zhifei Xie, Daniel Tang, Dingwei Tan +3
Current video generation models excel at creating short, realistic clips, but struggle with longer, multi-scene videos. We introduce \texttt{DreamFactory}, an LLM-based framework t…
Software Engineering for OpenHarmony: A Research Roadmap
Li Li, Xiang Gao, Hailong Sun +12
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 t…
On the Diffusion of Test Smells in LLM-Generated Unit Tests
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +5
LLMs promise to transform unit test generation from a manual burden into an automated solution. Yet, beyond metrics such as compilability or coverage, little is known about the qua…
An Empirical Study of AI Techniques in Mobile Applications
Yinghua Li, Xueqi Dang, Haoye Tian +5
The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized servic…
Bypassing the Combinatorial Explosion: Using Similarity to Generate and Prioritize T-wise Test Suites for Large Software Product Lines
Christopher Henard, Mike Papadakis, Gilles Perrouin +3
Software Product Lines (SPLs) are families of products whose commonalities and variability can be captured by Feature Models (FMs). T-wise testing aims at finding errors triggered…
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…
Software Security in Software-Defined Networking: A Systematic Literature Review
Moustapha Awwalou Diouf, Samuel Ouya, Jacques Klein +1
Software-defined networking (SDN) separates the control plane from the data plane and exposes the network through software applications and open APIs. The same programmability that…
Programming Language Confusion: When Code LLMs Can't Keep their Languages Straight
Micheline Bénédicte Moumoula, Serge Lionel Nikiema, Abdoul Kader Kabore +2
Large Language Models (LLMs) have achieved state-of-the-art performance across software engineering tasks, from code generation to translation. However, we identify and systematica…
In-Context Code-Text Learning for Bimodal Software Engineering
Xunzhu Tang, Liran Wang, Yonghui Liu +6
Bimodal software analysis initially appeared to be within reach with the advent of large language models. Unfortunately, the complex interplay of natural language text and code in…
Just-in-Time Detection of Silent Security Patches
Xunzhu Tang, Zhenghan Chen, Kisub Kim +3
Open-source code is pervasive. In this setting, embedded vulnerabilities are spreading to downstream software at an alarming rate. While such vulnerabilities are generally identifi…
Boosting Open-Source LLMs for Program Repair via Reasoning Transfer and LLM-Guided Reinforcement Learning
Xunzhu Tang, Jacques Klein, Tegawendé F. Bissyandé
Several closed-source LLMs have consistently outperformed open-source alternatives in program repair tasks, primarily due to their superior reasoning capabilities and extensive pre…
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode
Tiezhu Sun, Kevin Allix, Kisub Kim +5
The automation of a large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). Central to applying ML to software artifacts (like source or ex…
Rebooting Research on Detecting Repackaged Android Apps: Literature Review and Benchmark
Li Li, Tegawendé Bissyandé, Jacques Klein
Repackaging is a serious threat to the Android ecosystem as it deprives app developers of their benefits, contributes to spreading malware on users' devices, and increases the work…
Demystifying Hidden Sensitive Operations in Android apps
Xiaoyu Sun, Xiao Chen, Li Li +5
Security of Android devices is now paramount, given their wide adoption among consumers. As researchers develop tools for statically or dynamically detecting suspicious apps, malwa…
GAPS: Targeted Execution of Android Apps via Static Path Reconstruction
Samuele Doria, Eleonora Losiouk, Alexander Pilgun +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…
Empirical Study for Structured Output Control in LLMs for Software Engineering
Yewei Song, Prateek Rajput, Tiezhu Sun +3
LLM-generated outputs in software engineering rarely exist in isolation. They must plug into toolchains, APIs, and data pipelines that impose strict, often organization-specific st…
Old Tricks, New Models: How Simple Image Transformations Break Modern AI-based Content Moderation
Marco Alecci, Francesco Marchiori, Iyiola Emmanuel Olatunji +2
The paper evaluates three commercial image‑moderation services built on foundation models and shows that simple, model‑agnostic image transformations (e.g., color inversion, graysc…
How Secure is Secure Code Generation? Adversarial Prompts Put LLM Defenses to the Test
Melissa Tessa, Iyiola E. Olatunji, Aicha War +2
Recent secure code generation methods, using vulnerability-aware fine-tuning, prefix-tuning, and prompt optimization, claim to prevent LLMs from producing insecure code. However, t…
Dynamic Stability of LLM-Generated Code
Prateek Rajput, Abdoul Aziz Bonkoungou, Yewei Song +4
Current evaluations of LLMs for code generation emphasize functional correctness, overlooking the fact that functionally correct solutions can differ significantly in algorithmic c…
Explainable AI for Analyzing Person-Specific Patterns in Facial Recognition Tasks
PaweÅ 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…
Memorization or Interpolation ? Detecting LLM Memorization through Input Perturbation Analysis
Albérick Euraste Djiré, Abdoul Kader Kaboré, Earl T. Barr +2
While Large Language Models (LLMs) achieve remarkable performance through training on massive datasets, they can exhibit concerning behaviors such as verbatim reproduction of train…
Do Large Language Models Grasp The Grammar? Evidence from Grammar-Book-Guided Probing in Luxembourgish
Lujun Li, Yewei Song, Lama Sleem +7
Grammar refers to the system of rules that governs the structural organization and the semantic relations among linguistic units such as sentences, phrases, and words within a give…
Is Small Language Model the Silver Bullet to Low-Resource Languages Machine Translation?
Yewei Song, Lujun Li, Cedric Lothritz +6
Low-resource languages (LRLs) lack sufficient linguistic resources and are underrepresented in benchmark datasets, resulting in persistently lower translation quality than high-res…
Beep: Fine-grained Fix Localization by Learning to Predict Buggy Code Elements
Shangwen Wang, Kui Liu, Bo Lin +4
Software Fault Localization refers to the activity of finding code elements (e.g., statements) that are related to a software failure. The state-of-the-art fault localization techn…
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…
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…
Letz Translate: Low-Resource Machine Translation for Luxembourgish
Yewei Song, Saad Ezzini, Jacques Klein +3
Natural language processing of Low-Resource Languages (LRL) is often challenged by the lack of data. Therefore, achieving accurate machine translation (MT) in a low-resource enviro…
D&C: A Divide-and-Conquer Approach to IR-based Bug Localization
Anil Koyuncu, Tegawendé F. Bissyandé, Dongsun Kim +4
Many automated tasks in software maintenance rely on information retrieval techniques to identify specific information within unstructured data. Bug localization is such a typical…
Multilevel Semantic Embedding of Software Patches: A Fine-to-Coarse Grained Approach Towards Security Patch Detection
Xunzhu Tang, zhenghan Chen, Saad Ezzini +4
The growth of open-source software has increased the risk of hidden vulnerabilities that can affect downstream software applications. This concern is further exacerbated by softwar…
CREF: An LLM-based Conversational Software Repair Framework for Programming Tutors
Boyang Yang, Haoye Tian, Weiguo Pian +5
Program repair techniques offer cost-saving benefits for debugging within software development and programming education scenarios. With the proven effectiveness of Large Language…
Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance
Yewei Song, Cedric Lothritz, Daniel Tang +2
This paper revisits recent code similarity evaluation metrics, particularly focusing on the application of Abstract Syntax Tree (AST) editing distance in diverse programming langua…
FixMiner: Mining Relevant Fix Patterns for Automated Program Repair
Anil Koyuncu, Kui Liu, Tegawendé F. Bissyandé +4
Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurr…
Characterizing Build Compromises Through Vulnerability Disclosure Analysis
Maimouna Tamah Diao, Moustapha Awwalou Diouf, Iyiola Emmanuel Olatunji +4
The software build process transforms source code into deployable artifacts, representing a critical yet vulnerable stage in software development. Build infrastructure security pos…
The Struggles of LLMs in Cross-lingual Code Clone Detection
Micheline Bénédicte Moumoula, Abdoul Kader Kabore, Jacques Klein +1
With the involvement of multiple programming languages in modern software development, cross-lingual code clone detection has gained traction within the software engineering commun…
In-Vivo Bytecode Instrumentation for Improving Privacy on Android Smartphones in Uncertain Environments
Alexandre Bartel, Jacques Klein, Martin Monperrus +2
In this paper we claim that an efficient and readily applicable means to improve privacy of Android applications is: 1) to perform runtime monitoring by instrumenting the applicati…
LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning
Tiezhu Sun, Weiguo Pian, Nadia Daoudi +3
Transfomer-based models have significantly advanced natural language processing, in particular the performance in text classification tasks. Nevertheless, these models face challen…
Hyperbolic Code Retrieval: A Novel Approach for Efficient Code Search Using Hyperbolic Space Embeddings
Xunzhu Tang, zhenghan Chen, Saad Ezzini +4
Within the realm of advanced code retrieval, existing methods have primarily relied on intricate matching and attention-based mechanisms. However, these methods often lead to compu…
Beyond Surface Similarity: Evaluating LLM-Based Test Refactorings with Structural and Semantic Awareness
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +5
Large Language Models (LLMs) are increasingly used to refactor unit tests, improving readability and structure while preserving behavior. Evaluating such refactorings, however, rem…
Soft Prompt Tuning for Cross-Lingual Transfer: When Less is More
Fred Philippy, Siwen Guo, Shohreh Haddadan +3
Soft Prompt Tuning (SPT) is a parameter-efficient method for adapting pre-trained language models (PLMs) to specific tasks by inserting learnable embeddings, or soft prompts, at th…
MadDroid: Characterising and Detecting Devious Ad Content for Android Apps
Tianming Liu, Haoyu Wang, Li Li +6
Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, w…
ContractTrace: Retracing Smart Contract Versions for Security Analyses
Fatou Ndiaye Mbodji, Vinny Adjibi, Moustapha Awwalou Diouf +4
Due to the inherent immutability of blockchain technology, smart contract updates require their deployment at new addresses rather than modifying existing ones, thus fragmenting ve…
Unlocking LLM Repair Capabilities Through Cross-Language Translation and Multi-Agent Refinement
Wenqiang Luo, Jacky Wai Keung, Boyang Yang +4
Recent advances in leveraging LLMs for APR have demonstrated impressive capabilities in fixing software defects. However, current LLM-based approaches predominantly focus on mainst…
Adversarial Camouflage
PaweÅ Borsukiewicz, Daniele Lunghi, Melissa Tessa +2
While the rapid development of facial recognition algorithms has enabled numerous beneficial applications, their widespread deployment has raised significant concerns about the ris…
Anchor: Locating Android Framework-specific Crashing Faults
Pingfan Kong, Li Li, Jun Gao +4
Android framework-specific app crashes are hard to debug. Indeed, the callback-based event-driven mechanism of Android challenges crash localization techniques that are developed f…
Negative Results of Fusing Code and Documentation for Learning to Accurately Identify Sensitive Source and Sink Methods An Application to the Android Framework for Data Leak Detection
Jordan Samhi, Maria Kober, Abdoul Kader Kabore +3
Apps on mobile phones manipulate all sorts of data, including sensitive data, leading to privacy-related concerns. Recent regulations like the European GDPR provide rules for the p…
Learning to Catch Security Patches
Arthur D. Sawadogo, Tegawendé F. Bissyandé, Naouel Moha +4
Timely patching is paramount to safeguard users and maintainers against dire consequences of malicious attacks. In practice, patching is prioritized following the nature of the cod…
FlexiRepair: Transparent Program Repair with Generic Patches
Anil Koyuncu, Tegawendé F. Bissyandé, Jacques Klein +1
Template-based program repair research is in need for a common ground to express fix patterns in a standard and reusable manner. We propose to build on the concept of generic patch…
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…
Impact of Tool Support in Patch Construction
Anil Koyuncu, Tegawendé F. Bissyandé, Dongsun Kim +3
In this work, we investigate the practice of patch construction in the Linux kernel development, focusing on the differences between three patching processes: (1) patches crafted e…
Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation
Wendkûuni C. Ouédraogo, Kader Kaboré, Yinghua Li +5
Unit testing is essential for software reliability, yet manual test creation is time-consuming and often neglected. Search-based software testing improves efficiency but produces t…
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…
App Review Driven Collaborative Bug Finding
Xunzhu Tang, Haoye Tian, Pingfan Kong +3
Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., t…
Automatically Securing Permission-Based Software by Reducing the Attack Surface: An Application to Android
Alexandre Bartel, Jacques Klein, Martin Monperrus +1
A common security architecture, called the permission-based security model (used e.g. in Android and Blackberry), entails intrinsic risks. For instance, applications can be granted…
Evaluation Drift in LLM Personality Induction: Are We Moving the Goalpost?
Prateek Rajput, Yewei Song, Iyiola E. Olatunji +2
Can large language models reliably express a human-like personality, or are they merely mimicking surface cues without a stable underlying profile? To investigate this, we induce p…
iFixR: Bug Report driven Program Repair
Anil Koyuncu, Kui Liu, Tegawendé F. Bissyandé +4
Issue tracking systems are commonly used in modern software development for collecting feedback from users and developers. An ultimate automation target of software maintenance is…
DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware
Tiezhu Sun, Nadia Daoudi, Kisub Kim +3
Recent advancements in ML and DL have significantly improved Android malware detection, yet many methodologies still rely on basic static analysis, bytecode, or function call graph…
When English Isn't the Best Teacher: Source Language Effects in Cross-Lingual In-Context Learning
Fred Philippy, Siwen Guo, Jacques Klein +1
Cross-lingual transfer in multilingual NLP has been widely explored in supervised fine-tuning contexts, where factors like data availability and linguistic similarity largely deter…
Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG
Omar El Bachyr, Yewei Song, Saad Ezzini +5
PDF files are primarily intended for human reading rather than automated processing. In addition, the heterogeneous content of PDFs, such as text, tables, and images, poses signifi…
Is ChatGPT the Ultimate Programming Assistant -- How far is it?
Haoye Tian, Weiqi Lu, Tsz On Li +4
Recently, the ChatGPT LLM has received great attention: it can be used as a bot for discussing source code, prompting it to suggest changes, provide descriptions or even generate c…
Patch-CLIP: A Patch-Text Pre-Trained Model
Xunzhu Tang, Zhenghan Chen, Saad Ezzini +3
In recent years, patch representation learning has emerged as a necessary research direction for exploiting the capabilities of machine learning in software generation. These repre…
A Comparative Study of Smartphone and Smart TV Apps
Yonghui Liu, Xiao Chen, Yue Liu +6
Context: Smart TVs have become one of the most popular television types. Many app developers and service providers have designed TV versions for their smartphone applications. Desp…
Learning to Represent Patches
Xunzhu Tang, Haoye Tian, Zhenghan Chen +6
Patch representation is crucial in automating various software engineering tasks, like determining patch accuracy or summarizing code changes. While recent research has employed de…
Can LLMs Test Terminal User Interfaces?
Chao Peng, Ruida Hu, Ajitha Rajan +4
Terminal User Interfaces (TUIs) combine the stateful, screen-oriented behaviour of GUIs with terminal deployment and are now common in developer tools. Yet they lack a dedicated te…
Have I Seen You? Embedding Behavior Signals Synthetic Face Dataset Membership
PaweŠBorsukiewicz, Daniele Lunghi, Wendkûuni C. Ouédraogo +2
Synthetic face datasets are increasingly used to reduce privacy exposure and data access constraints in biometric recognition. Yet the generators that produce these datasets are tr…
A two-steps approach to improve the performance of Android malware detectors
Nadia Daoudi, Kevin Allix, Tegawendé F. Bissyandé +1
The popularity of Android OS has made it an appealing target to malware developers. To evade detection, including by ML-based techniques, attackers invest in creating malware that…
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…
GapFuzz: Cross-Plane Divergence Fuzzing for Distributed SDN Controllers
Moustapha Awwalou Diouf, Samuel Ouya, Jacques Klein +2
Distributed Software-Defined Networking (SDN) clusters replicate flow state asynchronously between a master node and its backups, leaving a window during which two backup nodes can…
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…
You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems
Kui Liu, Anil Koyuncu, Tegawendé F. Bissyandé +3
Properly benchmarking Automated Program Repair (APR) systems should contribute to the development and adoption of the research outputs by practitioners. To that end, the research c…
(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…