Machine learning (ML) systems in finance raise concerns about fairness, bias, and regulatory compliance, especially in high-stakes areas like creditworthiness, lending, and risk assessment. Bias in ML can lead to systemic economic exclusion, disproportionately affecting marginalized communities. This paper explores fairness adequacy testing in financial ML models, addressing regulatory, technical, and ethical challenges while proposing bias mitigation strategies. Financial ML models face unique constraints, including strict privacy regulations such as the General Data Protection Regulation (GDPR), the Equal Credit Opportunity Act (ECOA) in the U.S., and Basel Committee fairness standards, along with reliance on proprietary data. While this work provides an overview of fairness challenges, the detailed scoping review will be conducted in a subsequent study to further analyze regulatory frameworks and industry practices. This paper aims to highlight key gaps and considerations that must be addressed to balance fairness and accuracy in ML-driven financial decision-making.

Large Language Models (LLMs) have made significant strides in Natural Language Processing but remain vulnerable to fairness-related issues, often reflecting biases inherent in their training data. These biases pose risks, particularly when LLMs are deployed in sensitive areas such as healthcare, finance, and law. This paper introduces a metamorphic testing approach to systematically identify fairness bugs in LLMs. We define and apply a set of fairness-oriented metamorphic relations (MRs) to assess the LLaMA and GPT model, a state-of-the-art LLM, across diverse demographic inputs. Our methodology includes generating source and follow-up test cases for each MR and analyzing model responses for fairness violations. The results demonstrate the effectiveness of MT in exposing bias patterns, especially in relation to tone and sentiment, and highlight specific intersections of sensitive attributes that frequently reveal fairness faults. This research improves fairness testing in LLMs, providing a structured approach to detect and mitigate biases and improve model robustness in fairness-sensitive applications.

Summary Metamorphic testing (MT) is widely used for testing programs that face the oracle problem. It uses a set of metamorphic relations (MRs), which are relations among multiple inputs and their corresponding outputs to determine whether the program under test is faulty. Typically, MRs vary in their ability to detect faults in the program under test, and some MRs tend to detect the same set of faults. In this paper, we propose approaches to prioritize MRs to improve the efficiency and effectiveness of MT for regression testing. We present two MR prioritization approaches: (i) fault‐based and (ii) coverage‐based. To evaluate these MR prioritization approaches, we conduct experiments on three complex open‐source software systems. Our results show that the MR prioritization approaches developed by us significantly outperform the current practice of executing the source and follow‐up test cases of the MRs in an ad‐hoc manner in terms of fault detection effectiveness. Further, fault‐based MR prioritization leads to reducing the number of source and follow‐up test cases that needs to be executed as well as reducing the average time taken to detect a fault, which would result in saving time and cost during the testing process.

This paper presents how to generate an optimized sequence diagram from static java source code and dynamic execution trace at a web-based educational programming environment. The aim of this research is to help student programmers better understand the dynamic behavior of a java program using optimized sequence diagram, therefore to enhance object-oriented programming learning experience.

In large project, source code becomes increasing complex and lengthy so program comprehension plays an important and significant role for developers. Sequence diagram generated using static source code or dynamic only approach provides limited execution coverage, additionally contains redundant, dead and fault driven methods, which increase the size of the diagram and complexity. In this paper, to address the problems, optimized sequence diagrams were developed by combining static source code and dynamic only approach, also incorporating various levels of abstraction in order to reduce complexity and provide complete behavior of the system. Case studies determined from the sequence diagram for three systems generated based on source code only and fully dynamic approach proved that the proposed optimized sequence diagrams were less complex and provided more detailed description of the functionality of the system.