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Software effort is a measure of manpower dedicated to developing and maintaining and software. Effort estimation can help project managers monitor their software, teams, and timelines. Conversely, improper effort estimation can result in budget overruns, delays, lost contracts, and accumulated Technical Debt (TD). Issue Tracking Systems (ITS) have become mainstream project management tools, with over 65,000 companies using Jira alone. ITS are an untapped resource for issue resolution effort research. Related work investigates issue effort for specific issue types, usually Bugs or similar. They model their developer-documented issue resolution times using features from the issues themselves. This thesis explores a novel issue effort estimation and prediction approach using developer-documented ITS effort in tandem with implementation metrics (commit metrics, package metrics, refactoring metrics, and smell metrics). We find consistent correlations between ITS effort and implementation metrics, ranging from weak to moderate strength. We also construct and evaluate several exploratory models to predict future package effort using our novel effort estimation, with inconclusive results.

One of the key reasons that agile software development methods have gained popularity in recent years is because they enable organizations to produce software quickly to meet the needs of various stakeholders. However, this focus on delivering software quickly often encourages practitioners to incur technical debt – design or implementation constructs that are expedient in the short term but set up a technical context that can make future changes more costly or impossible. Worldwide, technical debt is estimated to be a trillion-dollar problem. This has prompted significant interest from both researchers and practitioners. In this dissertation, I present two essays that advance our knowledge of the causes of technical debt in agile software development projects and that offer potential solutions to manage the most important of these causes of technical debt. In my first essay, I conduct a ranking-type Delphi study of information technology (IT) project managers and software developers to identify and prioritize the most important causes of technical debt in agile software development projects. The findings from this study provide a verified list of 55 causes of technical debt in agile software development projects and offer 13 potential techniques to manage the causes of technical debt that were most important to the IT project managers and software developers in this study. In my second essay, I conduct a randomized experiment to examine the impact of software developers’ construal level, a cognitive process, on the unintentional accumulation of technical debt in software development projects. The findings from this experiment suggest that software developers at a high construal level are more likely to focus on developing the architecture or design than software developers at a low construal level. Collectively, the findings from these two essays deepen our understanding of the intentional and unintentional causes of technical debt in agile software development projects. Further, the findings offer potential techniques to manage the most important causes of technical debt for IT project managers and software developers.

Architecture degradation can have fundamental impact on software quality and productivity, resulting in inability to support new features, increasing technical debt and leading to significant losses. While code-level refactoring is widely-studied and well supported by tools, architecture-level refactorings, such as repackaging to group related features into one component, or retrofitting files into patterns, remain to be expensive and risky. Serval domains, such as Web services, heavily depend on complex architectures to design and implement interface-level operations, provided by several companies such as FedEx, eBay, Google, Yahoo and PayPal, to the end-users. The objectives of this work are: (1) to advance our ability to support complex architecture refactoring by explicitly defining Web service anti-patterns at various levels of abstraction, (2) to enable complex refactorings by learning from user feedback and creating reusable/personalized refactoring strategies to augment intelligent designers’ interaction that will guide low-level refactoring automation with high-level abstractions, and (3) to enable intelligent architecture evolution by detecting, quantifying, prioritizing, fixing and predicting design technical debts. We proposed various approaches and tools based on intelligent computational search techniques for (a) predicting and detecting multi-level Web services antipatterns, (b) creating an interactive refactoring framework that integrates refactoring path recommendation, design-level human abstraction, and code-level refactoring automation with user feedback using interactive mutli-objective search, and (c) automatically learning reusable and personalized refactoring strategies for Web services by abstracting recurring refactoring patterns from Web service releases. Based on empirical validations performed on both large open source and industrial services from multiple providers (eBay, Amazon, FedEx and Yahoo), we found that the proposed approaches advance our understanding of the correlation and mutual impact between service antipatterns at different levels, revealing when, where and how architecture-level anti-patterns the quality of services. The interactive refactoring framework enables, based on several controlled experiments, human-based, domain-specific abstraction and high-level design to guide automated code-level atomic refactoring steps for services decompositions. The reusable refactoring strategy packages recurring refactoring activities into automatable units, improving refactoring path recommendation and further reducing time-consuming and error-prone human intervention.