Deodorants are different from perfumes, because they are applied directly on body and by killing bacteria they reduce odours and offer a refreshing fragrance. That was our goal when we first thought about "bad smells" in code: to develop techniques for effectively identifying and removing (i.e., deodorizing) code smells from object-oriented software. JDeodorant encompasses a number of techniques for suggesting and automatically applying refactoring opportunities on Java source code, in a way that requires limited effort on behalf of the developer. In contrast to other approaches that rely on generic strategies that can be adapted to various smells, JDeodorant adopts ad-hoc strategies for each smell considering the particular characteristics of the underlying design or code problem. In this retrospective paper, we discuss the impact of JDeodorant over the last ten years and a number of tools and techniques that have been developed for a similar purpose which either compare their results with JDeodorant or have built on top of JDeodorant. Finally, we discuss the empirical findings from a number of studies that employed JDeodorant to extract their datasets.

The evolution of networks representing systems in various domains, including social networks, has been extensively studied enabling the development of growth models which govern their behavior over time. The architecture of software systems can also be naturally represented in the form of networks, whose properties change as software evolves. In this paper we attempt to model several aspects of graphs representing object-oriented software systems as they evolve over a number of versions. The goal is to develop a prediction model by considering global phenomena such as preferential attachment, past evolutionary trends such as the tendency of classes to create fewer relations as they age, as well as domain knowledge in terms of principles that have to be followed in object-oriented design. The derived models can provide insight into the future trends of software and potentially form the basis for eliciting improved or novel laws of software evolution. The forecasting power of the proposed model is evaluated against the actual evolution of 10 open-source projects and the achieved accuracy in the prediction of several network and software properties, which reflect the underlying system design, appears to be promising.

The analysis of software evolution by means of mining public repositories has been established as one of the dominant approaches for empirical studies in software engineering. However, even the investigation of the simplest research question demands a mazy process involving installation and configuration of tools, climbing their learning curve and tedious collection of desired information. Acknowledging the need for effortless querying of remote repositories we introduce a Web-based 'one-click approach' to perform software evolution analysis of Git projects.

Accumulated technical debt can be alleviated by means of refactoring application aiming at architectural improvement. A prerequisite for wide scale refactoring application is the automated identification of the corresponding refactoring opportunities, or code smells. One of the major architectural problems that has received limited attention is the so called 'Refused Bequest' which refers to inappropriate use of inheritance in object-oriented systems. This code smell occurs when subclasses do not take advantage of the inherited behavior, implying that replacement by delegation should be used instead. In this paper we propose a technique for the identification of Refused Bequest code smells whose major novelty lies in the intentional introduction of errors in the inherited methods. The essence of inheritance is evaluated by exercising the system's functionality through the corresponding unit tests in order to reveal whether inherited methods are actually employed by clients. Based on the results of this approach and other structural information, an indication of the smell strength on a 'thermometer' is obtained. The proposed approach has been implemented as an Eclipse plug in.

Evolving software systems can be systematically studied by treating them as networks and employing ideas and techniques from the field of Social Network Analysis. SEANets is an Eclipse plugin that allows the analysis of multiple software versions and the extraction and visualization of network properties required to investigate evolutionary trends of the underlying system.