Hitting the Bullseye: The Influence of Technical Debt on the Accuracy of Effort Estimation in Agile Projects

As firms rapidly develop solutions in order to increase revenue and market share, software development decisions considered to be temporary shortcuts and/or compromises may be implemented. These shortcuts represent “technical debt,” a metaphor which succinctly describes a software solution that should be “paid in full” or remediated in the future. Software architects and developers intend to resolve the “debt” in future product releases, but practitioners recognize that the challenge of always innovating may indefinitely postpone this remediation effort. Further, the accumulation of technical debt may have long term impact on the product’s maintainability by the software development teams and, consequently, impact the effort estimate delivered to management for forecasting product delivery timelines and product revenue expectations. While there are multiple publications that have studied effort estimation in traditional and agile software development strategies, there is limited research which considers technical debt during the estimation effort. As a result, the purpose of this dissertation is to design and propose a research model intended to determine whether or not the consideration of technical debt during the effort estimation process will improve the accuracy of the effort estimate in an agile project

Technical Debt Prioritization: State of the Art. A Systematic Literature Review

Background. Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring Technical Debt should be prioritized with respect to developing features or fixing bugs. Objective. The goal of this study is to investigate the existing body of knowledge in software engineering to understand what Technical Debt prioritization approaches have been proposed in research and industry. Method. We conducted a Systematic Literature Review among 384 unique papers published until 2018, following a consolidated methodology applied in Software Engineering. We included 38 primary studies. Results. Different approaches have been proposed for Technical Debt prioritization, all having different goals and optimizing on different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We report an impact map of such factors. However, there is a lack of empirical and validated set of tools. Conclusion. We observed that technical Debt prioritization research is preliminary and there is no consensus on what are the important factors and how to measure them. Consequently, we cannot consider current research conclusive and in this paper, we outline different directions for necessary future investigations

A Longitudinal Study of Identifying and Paying Down Architectural Debt

Architectural debt is a form of technical debt that derives from the gap between the architectural design of the system as it "should be" compared to "as it is". We measured architecture debt in two ways: 1) in terms of system-wide coupling measures, and 2) in terms of the number and severity of architectural flaws. In recent work it was shown that the amount of architectural debt has a huge impact on software maintainability and evolution. Consequently, detecting and reducing the debt is expected to make software more amenable to change. This paper reports on a longitudinal study of a healthcare communications product created by Brightsquid Secure Communications Corp. This start-up company is facing the typical trade-off problem of desiring responsiveness to change requests, but wanting to avoid the ever-increasing effort that the accumulation of quick-and-dirty changes eventually incurs. In the first stage of the study, we analyzed the status of the "before" system, which indicated the impacts of change requests. This initial study motivated a more in-depth analysis of architectural debt. The results of this analysis were used to motivate a comprehensive refactoring of the software system. The third phase of the study was a follow-on architectural debt analysis which quantified the improvements made. Using this quantitative evidence, augmented by qualitative evidence gathered from in-depth interviews with Brightsquid's architects, we present lessons learned about the costs and benefits of paying down architecture debt in practice.Comment: Submitted to ICSE-SEIP 201

A Quality Model for Actionable Analytics in Rapid Software Development

Background: Accessing relevant data on the product, process, and usage perspectives of software as well as integrating and analyzing such data is crucial for getting reliable and timely actionable insights aimed at continuously managing software quality in Rapid Software Development (RSD). In this context, several software analytics tools have been developed in recent years. However, there is a lack of explainable software analytics that software practitioners trust. Aims: We aimed at creating a quality model (called Q-Rapids quality model) for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies in order to determine relevant metrics as well as product and process factors. We also elicited how these metrics and factors are used and interpreted by practitioners when making decisions in RSD. We specified the Q-Rapids quality model by comparing and integrating the results of the four workshops. Then we implemented the Q-Rapids tool to support the usage of the Q-Rapids quality model as well as the gathering, integration, and analysis of the required data. Afterwards we installed the Q-Rapids tool in the four companies and performed semi-structured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceived the metrics as well as the product and process factors of the Q-Rapids quality model as understandable. Also, they considered the Q-Rapids quality model relevant for identifying product and process deficiencies (e.g., blocking code situations). Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model enables detecting problems that take more time to find manually and adds transparency among the perspectives of system, process, and usage.Comment: This is an Author's Accepted Manuscript of a paper to be published by IEEE in the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018. The final authenticated version will be available onlin

Applying ArchOptions to value the payoff of refactoring

ArchOptions is a real-options based model that we have pro-posed to value the flexibility of software architectures in response to future changes in requirements. In this paper, we build on ArchOptions to devise an options-based model, which values the architectural flexibility that results from a refactoring exercise. This value assists in understanding the payoff of investing in refactoring: if the refactored system results in an architecture that is more flexible, such that the expected added value (in the form of options) due to the en-hanced flexibility outweighs the cost of investing in this exer-cise, then refactoring is said to payoff. We apply our model to a refactoring case study from the literature

Should I Bug You? Identifying Domain Experts in Software Projects Using Code Complexity Metrics

In any sufficiently complex software system there are experts, having a deeper understanding of parts of the system than others. However, it is not always clear who these experts are and which particular parts of the system they can provide help with. We propose a framework to elicit the expertise of developers and recommend experts by analyzing complexity measures over time. Furthermore, teams can detect those parts of the software for which currently no, or only few experts exist and take preventive actions to keep the collective code knowledge and ownership high. We employed the developed approach at a medium-sized company. The results were evaluated with a survey, comparing the perceived and the computed expertise of developers. We show that aggregated code metrics can be used to identify experts for different software components. The identified experts were rated as acceptable candidates by developers in over 90% of all cases

Influence of developer factors on code quality: a data study

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Automatic source-code inspection tools help to assess, monitor and improve code quality. Since these tools only examine the software project’s codebase, they overlook other possible factors that may impact code quality and the assessment of the technical debt (TD). Our initial hypothesis is that human factors associated with the software developers, like coding expertise, communication skills, and experience in the project have some measurable impact on the code quality. In this exploratory study, we test this hypothesis on two large open source repositories, using TD as a code quality metric and the data that may be inferred from the version control systems. The preliminary results of our statistical analysis suggest that the level of participation of the developers and their experience in the project have a positive correlation with the amount of TD that they introduce. On the contrary, communication skills have barely any impact on TD.Peer ReviewedPostprint (author's final draft