19 citations · 57 across the 17 of their papers we have counts for
13 papers · 1 filter
Domain Adaptation for Code Model-based Unit Test Case Generation
Jiho Shin, Sepehr Hashtroudi, Hadi Hemmati +1
Recently, deep learning-based test case generation approaches have been proposed to automate the generation of unit test cases. In this study, we leverage Transformer-based code mo…
A First Look at Fairness of Machine Learning Based Code Reviewer Recommendation
Mohammad Mahdi Mohajer, Alvine Boaye Belle, Nima Shiri harzevili +5
The fairness of machine learning (ML) approaches is critical to the reliability of modern artificial intelligence systems. Despite extensive study on this topic, the fairness of ML…
FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair
Sakina Fatima, Hadi Hemmati, Lionel Briand
Flaky tests are problematic because they non-deterministically pass or fail for the same software version under test, causing confusion and wasting development effort. While machin…
A Systematic Literature Review of Explainable AI for Software Engineering
Ahmad Haji Mohammadkhani, Nitin Sai Bommi, Mariem Daboussi +3
Context: In recent years, leveraging machine learning (ML) techniques has become one of the main solutions to tackle many software engineering (SE) tasks, in research studies (ML4S…
Test2Vec: An Execution Trace Embedding for Test Case Prioritization
Emad Jabbar, Soheila Zangeneh, Hadi Hemmati +1
Most automated software testing tasks can benefit from the abstract representation of test cases. Traditionally, this is done by encoding test cases based on their code coverage. S…
An Empirical Study on Bug Severity Estimation using Source Code Metrics and Static Analysis
Ehsan Mashhadi, Shaiful Chowdhury, Somayeh Modaberi +2
In the past couple of decades, significant research efforts have been devoted to the prediction of software bugs (i.e., defects). In general, these works leverage a diverse set of…
Applying CodeBERT for Automated Program Repair of Java Simple Bugs
Ehsan Mashhadi, Hadi Hemmati
Software debugging, and program repair are among the most time-consuming and labor-intensive tasks in software engineering that would benefit a lot from automation. In this paper,…
A Search-Based Testing Framework for Deep Neural Networks of Source Code Embedding
Maryam Vahdat Pour, Zhuo Li, Lei Ma +1
Over the past few years, deep neural networks (DNNs) have been continuously expanding their real-world applications for source code processing tasks across the software engineering…
GloBug: Using Global Data in Fault Localization
Nima Miryeganeh, Sepehr Hashtroudi, Hadi Hemmati
Fault Localization (FL) is an important first step in software debugging and is mostly manual in the current practice. Many methods have been proposed over years to automate the FL…
A Pragmatic Approach for Hyper-Parameter Tuning in Search-based Test Case Generation
Shayan Zamani, Hadi Hemmati
Search-based test case generation, which is the application of meta-heuristic search for generating test cases, has been studied a lot in the literature, lately. Since, in theory,…
Revisiting Hyper-Parameter Tuning for Search-based Test Data Generation
Shayan Zamani, Hadi Hemmati
Search-based software testing (SBST) has been studied a lot in the literature, lately. Since, in theory, the performance of meta-heuristic search methods are highly dependent on th…
Interactive Semi-automated Specification Mining for Debugging: An Experience Report
Mohammad Jafar Mashhadi, Taha R. Siddiqui, Hadi Hemmati +1
Context: Specification mining techniques are typically used to extract the specification of a software in the absence of (up-to-date) specification documents. This is useful for pr…
An Empirical Study on Practicality of Specification Mining Algorithms on a Real-world Application
Mohammad Jafar Mashhadi, Hadi Hemmati
Dynamic model inference techniques have been the center of many research projects recently. There are now multiple open source implementations of state-of-the-art algorithms, which…