34 citations · 206 across the 21 of their papers we have counts for
21 papers
Characterizing the Landscape of Open-Source Satellite Software
Jinfeng Wen, Qi Liang, Yuehan Sun +4
Satellites have become fundamental components of modern technological systems, supporting critical infrastructure in communication, navigation, Earth observation, and scientific re…
Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
Saurabhsingh Rajput, Tim Widmayer, Ziyuan Shang +3
With the increasing usage, scale, and complexity of Deep Learning (DL) models, their rapidly growing energy consumption has become a critical concern. Promoting green development a…
Fairness Improvement with Multiple Protected Attributes: How Far Are We?
Zhenpeng Chen, Jie M. Zhang, Federica Sarro +1
Existing research mostly improves the fairness of Machine Learning (ML) software regarding a single protected attribute at a time, but this is unrealistic given that many users hav…
SCOPE: Performance Testing for Serverless Computing
Jinfeng Wen, Zhenpeng Chen, Jianshu Zhao +5
Serverless computing is a popular cloud computing paradigm that has found widespread adoption across various online workloads. It allows software engineers to develop cloud applica…
Assess and Summarize: Improve Outage Understanding with Large Language Models
Pengxiang Jin, Shenglin Zhang, Minghua Ma +13
Cloud systems have become increasingly popular in recent years due to their flexibility and scalability. Each time cloud computing applications and services hosted on the cloud are…
Unveiling Overlooked Performance Variance in Serverless Computing
Jinfeng Wen, Zhenpeng Chen, Federica Sarro +1
Serverless computing is an emerging cloud computing paradigm for developing applications at the function level, known as serverless functions. Due to the highly dynamic execution e…
Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances
Minghua Ma, Zhao Tian, Max Hort +4
Fairness testing aims at mitigating unintended discrimination in the decision-making process of data-driven AI systems. Individual discrimination may occur when an AI model makes d…
Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Second Replication Study (GPT2SP Replication Report)
Vali Tawosi, Rebecca Moussa, Federica Sarro
Fu and Tantithamthavorn have recently proposed GPT2SP, a Transformer-based deep learning model for SP estimation of user stories. They empirically evaluated the performance of GPT2…
Do Not Take It for Granted: Comparing Open-Source Libraries for Software Development Effort Estimation
Rebecca Moussa, Federica Sarro
In the past two decades, several Machine Learning (ML) libraries have become freely available. Many studies have used such libraries to carry out empirical investigations on predic…
Test-based Patch Clustering for Automatically-Generated Patches Assessment
Matias Martinez, Maria Kechagia, Anjana Perera +3
Previous studies have shown that Automated Program Repair (APR) techniques suffer from the overfitting problem. Overfitting happens when a patch is run and the test suite does not…
Fairness Testing: A Comprehensive Survey and Analysis of Trends
Zhenpeng Chen, Jie M. Zhang, Max Hort +2
Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedica…
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Max Hort, Zhenpeng Chen, Jie M. Zhang +2
This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias…
A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers
Zhenpeng Chen, Jie M. Zhang, Federica Sarro +1
Software bias is an increasingly important operational concern for software engineers. We present a large-scale, comprehensive empirical study of 17 representative bias mitigation…
A Versatile Dataset of Agile Open Source Software Projects
Vali Tawosi, Afnan Al-Subaihin, Rebecca Moussa +1
Agile software development is nowadays a widely adopted practise in both open-source and industrial software projects. Agile teams typically heavily rely on issue management tools…
On The Effectiveness of One-Class Support Vector Machine in Different Defect Prediction Scenarios
Rebecca Moussa, Danielle Azar, Federica Sarro
Defect prediction aims at identifying software components that are likely to cause faults before a software is made available to the end-user. To date, this task has been modeled a…
Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study
Vali Tawosi, Rebecca Moussa, Federica Sarro
In the last decade, several studies have explored automated techniques to estimate the effort of agile software development. We perform a close replication and extension of a semin…
A Survey on Machine Learning Techniques for Source Code Analysis
Tushar Sharma, Maria Kechagia, Stefanos Georgiou +4
The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis, such…
Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction Strategies
Giovani Guizzo, Federica Sarro, Jens Krinke +1
Mutation testing is an effective approach to evaluate and strengthen software test suites, but its adoption is currently limited by the mutants' execution computational cost. Sever…
Do Users Care about Ad's Performance Costs? Exploring the Effects of the Performance Costs of In-App Ads on User Experience
Cuiyun Gao, Jichuan Zeng, Federica Sarro +3
Context: In-app advertising is the primary source of revenue for many mobile apps. The cost of advertising (ad cost) is non-negligible for app developers to ensure a good user expe…
FrUITeR: A Framework for Evaluating UI Test Reuse
Yixue Zhao, Justin Chen, Adriana Sejfia +5
UI testing is tedious and time-consuming due to the manual effort required. Recent research has explored opportunities for reusing existing UI tests from an app to automatically ge…
A Framework for Genetic Algorithms Based on Hadoop
Filomena Ferrucci, M-Tahar Kechadi, Pasquale Salza +1
Genetic Algorithms (GAs) are powerful metaheuristic techniques mostly used in many real-world applications. The sequential execution of GAs requires considerable computational powe…