181 citations · 444 across the 7 of their papers we have counts for
8 papers · 1 filter
Testing Agentic Workflows with Structural Coverage Criteria
Nafiseh Kahani, Mojtaba Bagherzadeh
Multi-agent systems increasingly expose explicit workflow structure: agents, tools, tool-access rules, restrictions, and delegation paths. Existing evaluations rely largely on end-…
A Search-Based Testing Approach for Deep Reinforcement Learning Agents
Amirhossein Zolfagharian, Manel Abdellatif, Lionel Briand +2
Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving and robotic…
Black-Box Testing of Deep Neural Networks Through Test Case Diversity
Zohreh Aghababaeyan, Manel Abdellatif, Lionel Briand +2
Deep Neural Networks (DNNs) have been extensively used in many areas including image processing, medical diagnostics, and autonomous driving. However, DNNs can exhibit erroneous be…
Scalable and Accurate Test Case Prioritization in Continuous Integration Contexts
Ahmadreza Saboor Yaraghi, Mojtaba Bagherzadeh, Nafiseh Kahani +1
Continuous Integration (CI) requires efficient regression testing to ensure software quality without significantly delaying its CI builds. This warrants the need for techniques to…
Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review
Rongqi Pan, Mojtaba Bagherzadeh, Taher A. Ghaleb +1
Regression testing is an essential activity to assure that software code changes do not adversely affect existing functionalities. With the wide adoption of Continuous Integration…
Execution of Partial State Machine Models
Mojtaba Bagherzadeh, Nafiseh Kahani, Karim Jahed +1
The iterative and incremental nature of software development using models typically makes a model of a system incomplete (i.e., partial) until a more advanced and complete stage of…
Reinforcement Learning for Test Case Prioritization
Mojtaba Bagherzadeh, Nafiseh Kahani, Lionel Briand
Continuous Integration (CI) significantly reduces integration problems, speeds up development time, and shortens release time. However, it also introduces new challenges for qualit…
Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning
Hazem Fahmy, Fabrizio Pastore, Mojtaba Bagherzadeh +1
Deep neural networks (DNNs) are increasingly important in safety-critical systems, for example in their perception layer to analyze images. Unfortunately, there is a lack of method…