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In recent years, Deep Learning (DL) applications in JavaScript environment have become increasingly popular. As the infrastructure for DL applications, JavaScript DL frameworks play a crucial role in the development and deployment. It is…
This paper introduces the Java Software Evolution Tracker, a visualization and analysis tool that provides practitioners the means to examine the evolution of a software system from a top to bottom perspective, starting with changes in the…
Quantum computing offers significant speedups for simulating physical, chemical, and biological systems, and for optimization and machine learning. As quantum software grows in complexity, the classical simulation of quantum computers,…
Software systems evolve over their lifetime. Changing conditions, such as requirements or customer requests make it inevitable for developers to perform adjustments to the underlying code base. Especially in the context of open source…
Abstract: Failing test case reduction can promote efficient debugging because a developer may not need to observe components that are not relevant to inducing failure. Failing test case reduction can also improve the efficiency of fault…
Bugs that are detected earlier during the development lifecycle are easier and cheaper to fix, whereas bugs that are found during production are difficult and expensive to address, and may have dire consequences. Type systems are…
Microarray is a technology to quantitatively monitor the expression of large number of genes in parallel. It has become one of the main tools for global gene expression analysis in molecular biology research in recent years. The large…
Large language models (LLMs) have recently achieved significant success across various application domains, garnering substantial attention from different communities. Unfortunately, even for the best LLM, many \textit{faults} still exist…
High energy physics experiments including those at the Tevatron and the upcoming LHC require analysis of large data sets which are best handled by distributed computation. We present the design and development of a distributed data analysis…
Software testing is a critical, yet resource-intensive phase of the software development lifecycle. Over the years, various automated tools have been developed to aid in this process. Search-based approaches typically achieve high coverage…
In this contribution, we discuss the modeling and model reduction framework known as the Loewner framework. This is a data-driven approach, applicable to large-scale systems, which was originally developed for applications to linear…
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 languages such as Java or Python. While the results of…
Mutation analysis is a powerful technique for assessing test-suite adequacy, yet conventional approaches suffer from generating redundant, equivalent, or non-executable mutants. These challenges are particularly amplified in…
Unit tests are widely used to check source code quality, but they can be too coarse-grained or ill-suited for testing individual program statements. We introduce inline tests to make it easier to check for faults in statements. We motivate…
During software evolution, inexperienced developers may introduce design anti-patterns when they modify their software systems to fix bugs or to add new functionalities based on changes in requirements. Developers may also use design…
A common problem in machine learning is determining if a variable significantly contributes to a model's prediction performance. This problem is aggravated for datasets, such as gene expression datasets, that suffer the worst case of…
Machine translation (MT) systems that support low-resource languages often struggle on specialized domains. While researchers have proposed various techniques for domain adaptation, these approaches typically require model fine-tuning,…
Software testing is a very expensive and time consuming process. It can account for up to 50% of the total cost of the software development. Distributed systems make software testing a daunting task. The research described in this paper…
Despite best efforts, various challenges remain in the creation and maintenance processes of digital twins (DTs). One of those primary challenges is the constant, continuous and omnipresent evolution of systems, their user's needs and their…
Mutation analysis evaluates test suites and testing techniques by measuring how well they detect seeded defects (mutants). Even though well established in research, mutation analysis is rarely used in practice due to scalability problems…