265 citations · 369 across the 19 of their papers we have counts for
20 papers
An LLM Agentic Approach for Legal-Critical Software: A Case Study for Tax Prep Software
Sina Gogani-Khiabani, Ashutosh Trivedi, Diptikalyan Saha +1
Large language models (LLMs) show promise for translating natural-language statutes into executable logic, but reliability in legally critical settings remains challenging due to a…
Automated Validation of COBOL to Java Transformation
Atul Kumar, Diptikalyan Saha, Toshikai Yasue +6
Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterpriselevel code from legacy languages such as COBOL to modern l…
Automated Testing of COBOL to Java Transformation
Sandeep Hans, Atul Kumar, Toshikai Yasue +7
Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterprise-level code from legacy languages such as COBOL to modern…
Utilizing API Response for Test Refinement
Devika Sondhi, Ananya Sharma, Diptikalyan Saha
Most of the web services are offered in the form of RESTful APIs. This has led to an active research interest in API testing to ensure the reliability of these services. While most…
Interpretable Differencing of Machine Learning Models
Swagatam Haldar, Diptikalyan Saha, Dennis Wei +2
Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model…
DetAIL : A Tool to Automatically Detect and Analyze Drift In Language
Nishtha Madaan, Adithya Manjunatha, Hrithik Nambiar +4
Machine learning and deep learning-based decision making has become part of today's software. The goal of this work is to ensure that machine learning and deep learning-based syste…
Plug and Play Counterfactual Text Generation for Model Robustness
Nishtha Madaan, Srikanta Bedathur, Diptikalyan Saha
Generating counterfactual test-cases is an important backbone for testing NLP models and making them as robust and reliable as traditional software. In generating the test-cases, a…
Explainable Data Imputation using Constraints
Sandeep Hans, Diptikalyan Saha, Aniya Aggarwal
Data values in a dataset can be missing or anomalous due to mishandling or human error. Analysing data with missing values can create bias and affect the inferences. Several analys…
FROTE: Feedback Rule-Driven Oversampling for Editing Models
Öznur Alkan, Dennis Wei, Massimiliano Mattetti +3
Machine learning models may involve decision boundaries that change over time due to updates to rules and regulations, such as in loan approvals or claims management. However, in s…
Data Synthesis for Testing Black-Box Machine Learning Models
Diptikalyan Saha, Aniya Aggarwal, Sandeep Hans
The increasing usage of machine learning models raises the question of the reliability of these models. The current practice of testing with limited data is often insufficient. In…
Automated Testing of AI Models
Swagatam Haldar, Deepak Vijaykeerthy, Diptikalyan Saha
The last decade has seen tremendous progress in AI technology and applications. With such widespread adoption, ensuring the reliability of the AI models is crucial. In past, we too…
Towards API Testing Across Cloud and Edge
Samuel Ackerman, Sanjib Choudhury, Nirmit Desai +5
API economy is driving the digital transformation of business applications across the hybrid Cloud and edge environments. For such transformations to succeed, end-to-end testing of…
Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets
Nitin Gupta, Hima Patel, Shazia Afzal +10
The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Various tools and techniques are available that assess data qua…
Testing Framework for Black-box AI Models
Aniya Aggarwal, Samiulla Shaikh, Sandeep Hans +3
With widespread adoption of AI models for important decision making, ensuring reliability of such models remains an important challenge. In this paper, we present an end-to-end gen…
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text
Nishtha Madaan, Inkit Padhi, Naveen Panwar +1
Machine Learning has seen tremendous growth recently, which has led to larger adoption of ML systems for educational assessments, credit risk, healthcare, employment, criminal just…
Verifying Individual Fairness in Machine Learning Models
Philips George John, Deepak Vijaykeerthy, Diptikalyan Saha
We consider the problem of whether a given decision model, working with structured data, has individual fairness. Following the work of Dwork, a model is individually biased (or un…
Bias Mitigation Post-processing for Individual and Group Fairness
Pranay K. Lohia, Karthikeyan Natesan Ramamurthy, Manish Bhide +3
Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both in…
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind +15
Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and priso…
Automated Test Generation to Detect Individual Discrimination in AI Models
Aniya Agarwal, Pranay Lohia, Seema Nagar +2
Dependability on AI models is of utmost importance to ensure full acceptance of the AI systems. One of the key aspects of the dependable AI system is to ensure that all its decisio…
A simple and efficient explicit parallelization of logic programs using low-level threading primitives
Diptikalyan Saha, Paul Fodor
In this work, we present an automatic way to parallelize logic programs for finding all the answers to queries using a transformation to low level threading primitives. Although mu…