Structured Review of the Evidence for Effects of Code Duplication on Software Quality

This report presents the detailed steps and results of a structured review of code clone literature. The aim of the review is to investigate the evidence for the claim that code duplication has a negative effect on code changeability. This report contains only the details of the review for which there is not enough place to include them in the companion paper published at a conference (Hordijk, Ponisio et al. 2009 - Harmfulness of Code Duplication - A Structured Review of the Evidence)

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

Harmfulness of Code Duplication - A Structured Review of the Evidence

Duplication of code has long been thought to decrease changeability of systems, but recently doubts have been expressed whether this is true in general. This is a problem for researchers because it makes the value of research aimed against clones uncertain, and for practitioners as they cannot be sure whether their effort in reducing duplication is well-spent. In this paper we try to shed light on this is-sue by collecting empirical evidence in favor and against the nega-tive effects of duplication on changeability. We go beyond the flat yes/no-question of harmfulness and present an explanatory model to show the mechanisms through which duplication is suspected to affect quality. We aggregate the evidence for each of the causal links in the model. This sheds light on the current state of duplication re-search and helps practitioners choose between the available mitiga-tion strategies

An empirical study of evolution of inheritance in Java OSS

Previous studies of Object-Oriented (OO) software have reported avoidance of the inheritance mechanism and cast doubt on the wisdom of ‘deep’ inheritance levels. From an evolutionary perspective, the picture is unclear - we still know relatively little about how, over time, changes tend to be applied by developers. Our conjecture is that an inheritance hierarchy will tend to grow ‘breadth-wise’ rather than ‘depth-wise’. This claim is made on the basis that developers will avoid extending depth in favour of breadth because of the inherent complexity of having to understand the functionality of superclasses. Thus the goal of our study is to investigate this empirically. We conduct an empirical study of seven Java Open-Source Systems (OSSs) over a series of releases to observe the nature and location of changes within the inheritance hierarchies. Results show a strong tendency for classes to be added at levels one and two of the hierarchy (rather than anywhere else). Over 96% of classes added over the course of the versions of all systems were at level 1 or level 2. The results suggest that changes cluster in the shallow levels of a hierarchy; this is relevant for developers since it indicates where remedial activities such as refactoring should be focused