activity
20232026
most citedHow Effectively Do LLMs Extract Feature-Sentiment Pairs from App Reviews?

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2026

In the Driver's Seat: A Multi-Company Study on the Reality of Autonomous Driving System Testing

Qunying Song, Yuan Gao, Johannes Betz +3

Autonomous driving systems (ADS) are increasingly deployed in complex real-world environments, creating strong demands for rigorous testing to establish their functionality and saf…

cs.LG2026★ 1 cited

The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data -- a Systematic Review

Maj-Annika Tammisto, Faiz Ali Shah, Daniel Rodriguez +1

Background: High-level system testing of applications that use data from e-Government services as input requires test data that is real-life-like but where the privacy of personal…

cs.RO2025

An LSTM-based Test Selection Method for Self-Driving Cars

Ali Güllü, Faiz Ali Shah, Dietmar Pfahl

Self-driving cars require extensive testing, which can be costly in terms of time. To optimize this process, simple and straightforward tests should be excluded, focusing on challe…

cs.CL2024★ 2 cited

How Effectively Do LLMs Extract Feature-Sentiment Pairs from App Reviews?

Faiz Ali Shah, Ahmed Sabir, Rajesh Sharma +1

Automatic analysis of user reviews to understand user sentiments toward app functionality (i.e. app features) helps align development efforts with user expectations and needs. Rece…

cs.SE2023★ 1 cited

Towards a Complete Metamorphic Testing Pipeline

Alejandra Duque-Torres, Dietmar Pfahl

Metamorphic Testing (MT) addresses the test oracle problem by examining the relationships between input-output pairs in consecutive executions of the System Under Test (SUT). These…