31 citations · 71 across the 26 of their papers we have counts for
25 papers · 1 filter
Enhancing Automated Unit Test Generation for NLP Libraries Using Large Language Models
Amirhossein Deljouyi, Annibale Panichella, Andy Zaidman
Automated unit test generation tools like EvoSuite perform well on general-purpose software but often struggle with domain-specific software such as Natural Language Processing (NL…
Observability and Fault Injection for LLM-Based Multi-Agent Systems in Software Engineering
Zahra Seyedghorban, Egor Klimov, Arie van Deursen +2
Large Language Model-based multi-agent systems are increasingly explored for software engineering tasks, but they remain difficult to inspect, debug, and evaluate under controlled…
Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Task
Enrique Barba Roque, Luís Cruz, Annibale Panichella
Background: Large Language Models (LLMs) are increasingly being applied to Software Engineering (SE) tasks, achieving high accuracy across problems such as clone detection, vulnera…
Test Case Selection for Deep Neural Networks: A Replication Study on LLMs for Code
Ali Asgari, Mitchell Olsthoorn, Annibale Panichella
Recently, test case selection (TCS) techniques have been explored to support the operational evaluation of deep neural networks (DNNs) under limited testing budgets, where labeling…
A Metamorphic Testing Approach to Diagnosing Memorization in LLM-Based Program Repair
Milan De Koning, Ali Asgari, Pouria Derakhshanfar +1
LLM-based automated program repair (APR) techniques have shown promising results in reducing debugging costs. However, prior results can be affected by data leakage: large language…
Real-World Fault Detection for C-Extended Python Projects with Automated Unit Test Generation
Lucas Berg, Lukas Krodinger, Stephan Lukasczyk +4
Many popular Python libraries use C-extensions for performance-critical operations allowing users to combine the best of the two worlds: The simplicity and versatility of Python an…
What Challenges Do Developers Face in AI Agent Systems? An Empirical Study on Stack Overflow & GitHub Issues
Ali Asgari, Annibale Panichella, Pouria Derakhshanfar +1
AI Agents have rapidly gained prominence in both research and industry as systems that extend large language models with planning, tool use, memory, and goal-directed action. Despi…
Metamorphic Testing of Deep Code Models: A Systematic Literature Review
Ali Asgari, Milan de Koning, Pouria Derakhshanfar +1
Large language models and deep learning models designed for code intelligence have revolutionized the software engineering field due to their ability to perform various code-relate…
Automated Test-Case Generation for REST APIs Using Model Inference Search Heuristic
Clinton Cao, Annibale Panichella, Sicco Verwer
The rising popularity of the microservice architectural style has led to a growing demand for automated testing approaches tailored to these systems. EvoMaster is a state-of-the-ar…
Test Wars: A Comparative Study of SBST, Symbolic Execution, and LLM-Based Approaches to Unit Test Generation
Azat Abdullin, Pouria Derakhshanfar, Annibale Panichella
Generating tests automatically is a key and ongoing area of focus in software engineering research. The emergence of Large Language Models (LLMs) has opened up new opportunities, g…
The Last Dependency Crusade: Solving Python Dependency Conflicts with LLMs
Antony Bartlett, Cynthia Liem, Annibale Panichella
Resolving Python dependency issues remains a tedious and error-prone process, forcing developers to manually trial compatible module versions and interpreter configurations. Existi…
CATMA: Conformance Analysis Tool For Microservice Applications
Clinton Cao, Simon Schneider, Nicolás E. Díaz Ferreyra +3
The microservice architecture allows developers to divide the core functionality of their software system into multiple smaller services. However, this architectural style also mak…
Evolutionary Generative Fuzzing for Differential Testing of the Kotlin Compiler
Calin Georgescu, Mitchell Olsthoorn, Pouria Derakhshanfar +2
Compiler correctness is a cornerstone of reliable software development. However, systematic testing of compilers is infeasible, given the vast space of possible programs and the co…
TestSpark: IntelliJ IDEA's Ultimate Test Generation Companion
Arkadii Sapozhnikov, Mitchell Olsthoorn, Annibale Panichella +2
Writing software tests is laborious and time-consuming. To address this, prior studies introduced various automated test-generation techniques. A well-explored research direction i…
Improving Test Case Generation for REST APIs Through Hierarchical Clustering
Dimitri Stallenberg, Mitchell Olsthoorn, Annibale Panichella
With the ever-increasing use of web APIs in modern-day applications, it is becoming more important to test the system as a whole. In the last decade, tools and approaches have been…
Hybrid Multi-level Crossover for Unit Test Case Generation
Mitchell Olsthoorn, Pouria Derakhshanfar, Annibale Panichella
State-of-the-art search-based approaches for test case generation work at test case level, where tests are represented as sequences of statements. These approaches make use of gene…
Multi-objective Test Case Selection Through Linkage Learning-based Crossover
Mitchell Olsthoorn, Annibale Panichella
Test Case Selection (TCS) aims to select a subset of the test suite to run for regression testing. The selection is typically based on past coverage and execution cost data. Resear…
Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments
Christian Birchler, Sajad Khatiri, Pouria Derakhshanfar +2
Testing with simulation environments helps to identify critical failing scenarios for self-driving cars (SDCs). Simulation-based tests are safer than in-field operational tests and…
JUGE: An Infrastructure for Benchmarking Java Unit Test Generators
Xavier Devroey, Alessio Gambi, Juan Pablo Galeotti +4
Researchers and practitioners have designed and implemented various automated test case generators to support effective software testing. Such generators exist for various language…
What Are We Really Testing in Mutation Testing for Machine Learning? A Critical Reflection
Annibale Panichella, Cynthia C. S. Liem
Mutation testing is a well-established technique for assessing a test suite's quality by injecting artificial faults into production code. In recent years, mutation testing has bee…
Search-Based Software Re-Modularization: A Case Study at Adyen
Casper Schröder, Adriaan van der Feltz, Annibale Panichella +1
Deciding what constitutes a single module, what classes belong to which module or the right set of modules for a specific software system has always been a challenging task. The pr…
Run, Forest, Run? On Randomization and Reproducibility in Predictive Software Engineering
Cynthia C. S. Liem, Annibale Panichella
Machine learning (ML) has been widely used in the literature to automate software engineering tasks. However, ML outcomes may be sensitive to randomization in data sampling mechani…
Generating Class-Level Integration Tests Using Call Site Information
Pouria Derakhshanfar, Xavier Devroey, Annibale Panichella +2
Search-based approaches have been used in the literature to automate the process of creating unit test cases. However, related work has shown that generated unit-tests with high co…
Scalable Inference of System-level Models from Component Logs
Donghwan Shin, Salma Messaoudi, Domenico Bianculli +3
Behavioral software models play a key role in many software engineering tasks; unfortunately, these models either are not available during software development or, if available, th…