7 citations · 36 across the 19 of their papers we have counts for
19 papers
Can LLMs Test Terminal User Interfaces?
Chao Peng, Ruida Hu, Ajitha Rajan +3
Terminal User Interfaces (TUIs) combine the stateful, screen-oriented behaviour of GUIs with terminal deployment and are now common in developer tools. Yet they lack a dedicated te…
Go-Oracle: Automated Test Oracle for Go Concurrency Bugs
Foivos Tsimpourlas, Chao Peng, Carlos Rosuero +2
The Go programming language has gained significant traction for developing software, especially in various infrastructure systems. Nonetheless, concurrency bugs have become a preva…
DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models
Nikolaos Louloudakis, Perry Gibson, José Cano +1
Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Fai…
Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition
Nikolaos Louloudakis, Perry Gibson, José Cano +1
When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is erro…
Exploring Robustness of Image Recognition Models on Hardware Accelerators
Nikolaos Louloudakis, Perry Gibson, José Cano +1
As the usage of Artificial Intelligence (AI) on resource-intensive and safety-critical tasks increases, a variety of Machine Learning (ML) compilers have been developed, enabling c…
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition
Xiaoliang Wu, Peter Bell, Ajitha Rajan
Explainable AI (XAI) techniques have been widely used to help explain and understand the output of deep learning models in fields such as image classification and Natural Language…
Vaxformer: Antigenicity-controlled Transformer for Vaccine Design Against SARS-CoV-2
Aryo Pradipta Gema, Michał Kobiela, Achille Fraisse +3
The SARS-CoV-2 pandemic has emphasised the importance of developing a universal vaccine that can protect against current and future variants of the virus. The present study propose…
Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks
Aryo Pradipta Gema, Dominik Grabarczyk, Wolf De Wulf +5
Knowledge graphs are powerful tools for representing and organising complex biomedical data. Several knowledge graph embedding algorithms have been proposed to learn from and compl…
BenchDirect: A Directed Language Model for Compiler Benchmarks
Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu +4
The exponential increase of hardware-software complexity has made it impossible for compiler engineers to find the right optimization heuristics manually. Predictive models have be…
Explanations for Automatic Speech Recognition
Xiaoliang Wu, Peter Bell, Ajitha Rajan
We address quality assessment for neural network based ASR by providing explanations that help increase our understanding of the system and ultimately help build trust in the syste…
Exploring Effects of Computational Parameter Changes to Image Recognition Systems
Nikolaos Louloudakis, Perry Gibson, José Cano +1
Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and FPGAs for fast, timely processing. Fa…
BenchPress: A Deep Active Benchmark Generator
Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu +4
We develop BenchPress, the first ML benchmark generator for compilers that is steerable within feature space representations of source code. BenchPress synthesizes compiling functi…
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities
Xin Du, Benedicte Legastelois, Bhargavi Ganesh +5
Using large pre-trained models for image recognition tasks is becoming increasingly common owing to the well acknowledged success of recent models like vision transformers and othe…
Catch Me If You Can: Blackbox Adversarial Attacks on Automatic Speech Recognition using Frequency Masking
Xiaoliang Wu, Ajitha Rajan
Automatic speech recognition (ASR) models are prevalent, particularly in applications for voice navigation and voice control of domestic appliances. The computational core of ASRs…
Interpreting BERT architecture predictions for peptide presentation by MHC class I proteins
Hans-Christof Gasser, Georges Bedran, Bo Ren +3
The major histocompatibility complex (MHC) class-I pathway supports the detection of cancer and viruses by the immune system. It presents parts of proteins (peptides) from inside a…
CAT: Change-focused Android GUI Testing
Chao Peng, Ajitha Rajan
Android Apps are frequently updated, every couple of weeks, to keep up with changing user, hardware and business demands. Correctness of App updates is checked through extensive te…
M3: Semantic API Migrations
Bruce Collie, Philip Ginsbach, Jackson Woodruff +2
Library migration is a challenging problem, where most existing approaches rely on prior knowledge. This can be, for example, information derived from changelogs or statistical mod…
Learning to Encode and Classify Test Executions
Foivos Tsimpourlas, Ajitha Rajan, Miltiadis Allamanis
The challenge of automatically determining the correctness of test executions is referred to as the test oracle problem and is one of the key remaining issues for automated testing…
SIF: A Framework for Solidity Code Instrumentation and Analysis
Chao Peng, Sefa Akca, Ajitha Rajan
Solidity is an object-oriented and high-level language for writing smart contracts that are used to execute, verify and enforce credible transactions on permissionless blockchains.…