14 citations · 47 across the 18 of their papers we have counts for
21 papers
Operationalizing Property-Based Testing for Data-Intensive Scalable Computing Systems
Yaoxuan Wu, Ingrid Lee, Ahmad Humayun +2
While fuzzing effectively catches crashes, its shallow oracles often miss semantic drifts and optimization-related errors in data-intensive scalable computing (DISC) frameworks. Pr…
Assessing Large Language Models for Stabilizing Numerical Expressions in Scientific Software
Tien Nguyen, Kirshanthan Sundararajah, Muhammad Ali Gulzar
Scientific software relies on high-precision computation, yet finite floating-point representations introduce precision errors that propagate in safety-critical domains. Despite gr…
Evaluating LLM-Based Test Generation Under Software Evolution
Sabaat Haroon, Mohammad Taha Khan, Muhammad Ali Gulzar
Large Language Models (LLMs) are increasingly used for automated unit test generation. However, it remains unclear whether these tests reflect genuine reasoning about program behav…
TENSURE: Fuzzing Sparse Tensor Compilers (Registered Report)
Kabilan Mahathevan, Yining Zhang, Muhammad Ali Gulzar +1
Sparse Tensor Compilers (STCs) have emerged as critical infrastructure for optimizing high-dimensional data analytics and machine learning workloads. The STCs must synthesize compl…
ProToken: Token-Level Attribution for Federated Large Language Models
Waris Gill, Ahmad Humayun, Ali Anwar +1
Federated Learning (FL) enables collaborative training of Large Language Models (LLMs) across distributed data sources while preserving privacy. However, when federated LLMs are de…
PALM: Path-aware LLM-based Test Generation with Comprehension
Yaoxuan Wu, Xiaojie Zhou, Ahmad Humayun +2
Symbolic execution is a widely used technique for test generation, offering systematic exploration of program paths through constraint solving. However, it is fundamentally constra…
Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data
Waris Gill, Justin Cechmanek, Tyler Hutcherson +5
This report investigates enhancing semantic caching effectiveness by employing specialized, fine-tuned embedding models. Semantic caching relies on embedding similarity rather than…
Assessing the Impact of Code Changes on the Fault Localizability of Large Language Models
Sabaat Haroon, Ahmad Faraz Khan, Ahmad Humayun +5
Generative Large Language Models (LLMs) are increasingly used in non-generative software maintenance tasks, such as fault localization (FL). Success in FL depends on a models abili…
Are the Majority of Public Computational Notebooks Pathologically Non-Executable?
Tien Nguyen, Waris Gill, Muhammad Ali Gulzar
Computational notebooks are the de facto platforms for exploratory data science, offering an interactive programming environment where users can create, modify, and execute code ce…
PerfGen: Automated Performance Benchmark Generation for Big Data Analytics
Jiyuan Wang, Jason Teoh, Muhammand Ali Gulza +2
Many symptoms of poor performance in big data analytics such as computational skews, data skews, and memory skews are input dependent. However, due to the lack of inputs that can t…
Accessibility Issues in Ad-Driven Web Applications
Abdul Haddi Amjad, Muhammad Danish, Bless Jah +1
Website accessibility is essential for inclusiveness and regulatory compliance. Although third-party advertisements (ads) are a vital revenue source for free web services, they int…
Blocking Tracking JavaScript at the Function Granularity
Abdul Haddi Amjad, Shaoor Munir, Zubair Shafiq +1
Modern websites extensively rely on JavaScript to implement both functionality and tracking. Existing privacy enhancing content blocking tools struggle against mixed scripts, which…
Human-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking
Hong Jin Kang, Fabrice Harel-Canada, Muhammad Ali Gulzar +2
Data augmentation techniques apply transformations to existing texts to generate additional data. The transformations may produce low-quality texts, where the meaning of the text i…
MeanCache: User-Centric Semantic Caching for LLM Web Services
Waris Gill, Mohamed Elidrisi, Pallavi Kalapatapu +3
Large Language Models (LLMs) like ChatGPT and Llama have revolutionized natural language processing and search engine dynamics. However, these models incur exceptionally high compu…
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance
Waris Gill, Ali Anwar, Muhammad Ali Gulzar
In Federated Learning, clients train models on local data and send updates to a central server, which aggregates them into a global model using a fusion algorithm. This collaborati…
FedDefender: Backdoor Attack Defense in Federated Learning
Waris Gill, Ali Anwar, Muhammad Ali Gulzar
Federated Learning (FL) is a privacy-preserving distributed machine learning technique that enables individual clients (e.g., user participants, edge devices, or organizations) to…
Blocking JavaScript without Breaking the Web: An Empirical Investigation
Abdul Haddi Amjad, Zubair Shafiq, Muhammad Ali Gulzar
Modern websites heavily rely on JavaScript (JS) to implement legitimate functionality as well as privacy-invasive advertising and tracking. Browser extensions such as NoScript bloc…
FedDebug: Systematic Debugging for Federated Learning Applications
Waris Gill, Ali Anwar, Muhammad Ali Gulzar
In Federated Learning (FL), clients independently train local models and share them with a central aggregator to build a global model. Impermissibility to access clients' data and…
Sibylvariant Transformations for Robust Text Classification
Fabrice Harel-Canada, Muhammad Ali Gulzar, Nanyun Peng +1
The vast majority of text transformation techniques in NLP are inherently limited in their ability to expand input space coverage due to an implicit constraint to preserve the orig…
TrackerSift: Untangling Mixed Tracking and Functional Web Resources
Abdul Haddi Amjad, Danial Saleem, Fareed Zaffar +2
Trackers have recently started to mix tracking and functional resources to circumvent privacy-enhancing content blocking tools. Such mixed web resources put content blockers in a b…
Efficient Fuzz Testing for Apache Spark Using Framework Abstraction
Qian Zhang, Jiyuan Wang, Muhammad Ali Gulzar +2
The emerging data-intensive applications are increasingly dependent on data-intensive scalable computing (DISC) systems, such as Apache Spark, to process large data. Despite their…