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Technical debt has been the subject of numerous studies over the last few years. To date, most of the research has concentrated on management (detection, quantification, and decision making) approaches -most performed at code and implementation levels through various static analysis tools. However, if practitioners are to adopt model driven techniques, then the management of technical debt also requires that we address this problem during the specification and architectural phases. This position paper discusses several questions that need to be addressed in order to improve the quality of software architecture by exploring the management of technical debt during modeling, and suggests various lines of research that are worthwhile subjects for further investigation.

Technical debt is a well understood yet understudied phenomena. A current issue is the verification and validation of proposed methods for technical debt management in the context of agile development. In practice, such evaluations are either too costly or too time consuming to be conducted using traditional empirical methods. In this paper, we describe a set of simulations based on models of the agile development process, Scrum, and the integration of technical debt management. The purpose of this study is to identify which strategy is superior and to provide empirical evidence to support existing claims. The models presented are based upon conceptual and industry models concerning defects and technical debt. The results of the simulations provide compelling evidence for current technical debt management strategies proposed in the literature that can be immediately applied by practitioners.

Measurements are subject to random and systematic errors, yet almost no study in software engineering makes significant efforts in reporting these errors. Whilst established statistical techniques are well suited for the analysis of random error, such techniques are not valid in the presence of systematic errors. We propose a departure from de-facto methods of reporting results of technical debt measurements for more rigorous techniques drawn from established methods in the physical sciences. This line of inquiry focuses on technical debt calculations; however it can be generalized to quantitative software engineering studies. We pose research questions and seek answers to the identification of systematic errors in metric-based tools, as well as the reporting of such errors when subjected to propagation. Exploratory investigations reveal that the techniques suggested allow for the comparison of uncertainties that come from differing sources. We suggest the study of error propagation of technical debt is a worthwhile subject for further research and techniques seeded from the physical sciences present viable options that can be used in software engineering reporting.

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