This inventor holds 1 USPTO granted patent. Top assignee: International Business Machines Corporation. Active years: 2005.
Company Filing History:
Years Active: 2005
Title: Larry Koved: Innovator in Software Analysis
Introduction
Larry Koved is a notable inventor based in Pleasantville, NY (US). He has made significant contributions to the field of software analysis, particularly in the area of mutability detection in programming languages. His work has implications for improving software reliability and maintainability.
Latest Patents
Larry Koved holds a patent for "Mutability analysis in Java." This patent describes a system and method for detecting the mutability of fields and classes in an arbitrary program component written in an object-oriented programming language. The invention provides a static analysis algorithm that can be applied to any software component, rather than requiring analysis of entire programs. The algorithm classifies fields and classes as either mutable or immutable, facilitating open-world analysis. It identifies situations that expose variables to potential modification by external code, as well as those modified by the analyzed code. The implementation focuses on detecting mutability of class variables to avoid isolation problems and incorporates intra- and inter-procedural data-flow analyses, demonstrating high scalability and effectiveness through experimental results.
Career Highlights
Larry Koved is associated with International Business Machines Corporation (IBM), where he has contributed to various projects and innovations in software development. His expertise in mutability analysis has positioned him as a key figure in enhancing software quality and performance.
Collaborations
Larry has worked alongside talented colleagues, including Marina Biberstein and Bilha Mendelson, contributing to a collaborative environment that fosters innovation and problem-solving.
Conclusion
Larry Koved's work in mutability analysis represents a significant advancement in software engineering. His contributions not only enhance the understanding of variable behavior in programming but also improve the overall quality of software systems.