Modeling User-Affected Software Properties for Open Source Software Supply Chains

Background: Open Source Software development community relies heavily on users of the software and contributors outside of the core developers to produce top-quality software and provide long-term support. However, the relationship between a software and its contributors in terms of exactly how they are related through dependencies and how the users of a software affect many of its properties are not very well understood. Aim: My research covers a number of aspects related to answering the overarching question of modeling the software properties affected by users and the supply chain structure of software ecosystems, viz. 1) Understanding how software usage affect its perceived quality; 2) Estimating the effects of indirect usage (e.g. dependent packages) on software popularity; 3) Investigating the patch submission and issue creation patterns of external contributors; 4) Examining how the patch acceptance probability is related to the contributors\u27 characteristics. 5) A related topic, the identification of bots that commit code, aimed at improving the accuracy of these and other similar studies was also investigated. Methodology: Most of the Research Questions are addressed by studying the NPM ecosystem, with data from various sources like the World of Code, GHTorrent, and the GiHub API. Different supervised and unsupervised machine learning models, including Regression, Random Forest, Bayesian Networks, and clustering, were used to answer appropriate questions. Results: 1) Software usage affects its perceived quality even after accounting for code complexity measures. 2) The number of dependents and dependencies of a software were observed to be able to predict the change in its popularity with good accuracy. 3) Users interact (contribute issues or patches) primarily with their direct dependencies, and rarely with transitive dependencies. 4) A user\u27s earlier interaction with the repository to which they are contributing a patch, and their familiarity with related topics were important predictors impacting the chance of a pull request getting accepted. 5) Developed BIMAN, a systematic methodology for identifying bots. Conclusion: Different aspects of how users and their characteristics affect different software properties were analyzed, which should lead to a better understanding of the complex interaction between software developers and users/ contributors

DEVELOPMENT OF A QUALITY MANAGEMENT ASSESSMENT TOOL TO EVALUATE SOFTWARE USING SOFTWARE QUALITY MANAGEMENT BEST PRACTICES

Organizations are constantly in search of competitive advantages in today’s complex global marketplace through improvement of quality, better affordability, and quicker delivery of products and services. This is significantly true for software as a product and service. With other things being equal, the quality of software will impact consumers, organizations, and nations. The quality and efficiency of the process utilized to create and deploy software can result in cost and schedule overruns, cancelled projects, loss of revenue, loss of market share, and loss of consumer confidence. Hence, it behooves us to constantly explore quality management strategies to deliver high quality software quickly at an affordable price. This research identifies software quality management best practices derived from scholarly literature using bibliometric techniques in conjunction with literature review, synthesizes these best practices into an assessment tool for industrial practitioners, refines the assessment tool based on academic expert review, further refines the assessment tool based on a pilot test with industry experts, and undertakes industry expert validation. Key elements of this software quality assessment tool include issues dealing with people, organizational environment, process, and technology best practices. Additionally, weights were assigned to issues of people, organizational environment, process, and technology best practices based on their relative importance, to calculate an overall weighted score for organizations to evaluate where they stand with respect to their peers in pursuing the business of producing quality software. This research study indicates that people best practices carry 40% of overall weight, organizational best v practices carry 30% of overall weight, process best practices carry 15% of overall weight, and technology best practices carry 15% of overall weight. The assessment tool that is developed will be valuable to organizations that seek to take advantage of rapid innovations in pursuing higher software quality. These organizations can use the assessment tool for implementing best practices based on the latest cutting edge management strategies that can lead to improved software quality and other competitive advantages in the global marketplace. This research contributed to the current academic literature in software quality by presenting a quality assessment tool based on software quality management best practices, contributed to the body of knowledge on software quality management, and expanded the knowledgebase on quality management practices. This research also contributed to current professional practice by incorporating software quality management best practices into a quality management assessment tool to evaluate software