Aparna is a graduate of the University of Maryland, Baltimore County’s Computer Science Master’s Program. She immersed herself in numerous projects that required a deep understanding of intricate programming concepts as well as thorough research. Her notable data analytics projects include a pilot study for quantitative data visualization in compartmented force-directed graphs using calibrated columns, an analysis of the k-mismatch problem in online and offline settings, and an evaluation of proposed hardware architecture improvements for network packet rule matching. She has also done research regarding copyright, piracy, and digital rights management in the music industry.

At DisputeSoft, Aparna assists in drafting expert, rebuttal, and investigative reports by reviewing disputed source code, databases, and computer systems, writing object-driven scripts and SQL queries, and conducting data analyses regarding source code quality and architecture. She also has experience with performing source code comparison, running static code analyses using tools such as Checkstyle, JArchitect, PMD, SonarQube, and Understand, querying data from database systems such as Microsoft SQL Server and Oracle, and analyzing operating system source code. Her thorough understanding of object-oriented computer concepts and relational databases allows her to effectively assist clients in the technical and analytical aspects of software related matters.

For example, Aparna supports software project failure cases by performing various analyses on project data from different stages of the software development life cycle, including analysis of requirements decomposition and traceability using sampling methods, programmatic analysis of design documents affected by design issues, source code traces, analysis of code quality metrics, querying of defect open and close rates, and identification of compliance with quality assurance documentation. She supports data analytics aspects of cases by writing complex SQL queries to extract and analyze relevant data from multiple data sources, tables, and databases.