Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

Context: The aim of statistical causal inference (SCI) methods is to estimate causal effects
from observational data (ie, when randomized controlled trials are not possible). In this …

Given a discriminating neural network, the problem of fairness improvement is to
systematically reduce discrimination without significantly scarifies its performance (ie …

There has been an increasing interest in enhancing the fairness of machine learning (ML).
Despite the growing number of fairness-improving methods, we lack a systematic …

Deep neural network (DNN) testing approaches have grown fast in recent years to test the
correctness and robustness of DNNs. In particular, DNN coverage criteria are frequently …

While code generation has been widely used in various software development scenarios,
the quality of the generated code is not guaranteed. This has been a particular concern in …

Debugging performance anomalies in databases is challenging. Causal inference
techniques enable qualitative and quantitative root cause analysis of performance …

Large Language Models (LLMs) such as GPT and Llama2 are increasingly adopted in many
safety-critical applications. Their security is thus essential. Even with considerable efforts …

Crowdsourcing Federated learning (CFL) is a new crowdsourcing development paradigm
for the Deep Neural Network (DNN) models, also called “software 2.0”. In practice, the …

Recently, neural networks have spread into numerous fields including many safety-critical
systems. Neural networks are built (and trained) by programming in frameworks such as …