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

Selecting Undergraduate Business Majors

The paper begins with a brief review of the literature and how business students choose their major in the U.S. and we list the most popular majors in the U.S. Universities. We also talk about the factors that influenced student’s choice. In our next research project, we will not only use a larger sample size but also the sample will come from a few universities to reduce the sampling bias. In this paper, we also talk about changing trends in international students. We talk about the large group of Chinese, Indian, and Arabic students, and we show that with literature and graphical support. In the next section, we analyze one of the up and coming new business majors ―Business Analytics‖ We finish the paper with a discussion of growth of international students both at graduate and undergraduate level, and how we will address the shortcomings of this paper with our next project

DATA-DRIVEN CHARACTERIZATION OF TECHNICAL DEBT IN A COMPLEX INFORMATION SYSTEM

Presented herein are techniques that provide a holistic and integrated abstraction among different categories of technical debt (TD) in a complex software system, as well as among different TD-related data sources such as logs, traces, telemetry, and metrics. The techniques presented herein allow for accelerated, automated, and evolutionary TD management in a complex software development life cycle (SDLC). The techniques learn the context throughout the SDLC pipeline and turn this context into actionable insights for use in repaying the technical debt at the earliest stages of the development process. The techniques presented herein provide an automated and low cost mechanism that may reduce debt within a company

SERVICE REQUEST SPEND BASED DATA-DRIVEN TECHNICAL DEBT CHARACTERIZATION IN COMPLEX SOFTWARE SYSTEMS

Techniques are described herein for a data-driven Technical Debt (TD) analytics platform that allows executives and product development teams to optimally track, manage, and repay TD early in the software development life cycle. This may improve team productivity, address the problem of software TD holistically in the code-to-customer lifecycle, and yield long term benefits. Thus, the platform provides additions to savings, improved customer experience, and enhanced serviceability

Institutional Analytics: A Response to the Pressures of Academic Capitalism

The higher education sector today faces an environment unlike any it has seen before. Serving a wide variety of internal and external stakeholders and facing diverse and fast-changing economic, social, and political pressures, universities can benefit from corporate-like approaches such as the use of analytics to inform strategic decision-making and planning. Institutional analytics programs can be a valuable resource in guiding university responses to modern challenges around fiscal responsibility, accountability, competition, and student success. Customizable when it comes to leadership, staffing, and data and technology infrastructure, analytics initiatives can be targeted to meet individual institutional resources, environments, challenges, needs, mission, and values. One such resource available at most institutions is Institutional Research (IR), a field that has undergone regular evolution to meet the changing needs of postsecondary education. The unique combination of technical, analytical, and interpersonal roles and skills needed for the effective use of data and analytics can often be met through the engagement of Institutional Research leaders and staff in these initiatives, and they are frequently key participants in the support and delivery of analytics efforts on campus. With Institutional Research as a resource, and flexibility in creating an analytics program that best meet the needs of individual institutions, analytics can serve as a powerful and effective tool for universities responding to todays’ pressures of academic capitalism