Nikolaos Tsantalis

Design Pattern Detection Using Similarity Scoring

The identification of design patterns as part of the reengineering process can convey important information to the designer. However, existing pattern detection methodologies generally have problems in dealing with one or more of the following issues: identification of modified pattern versions, search space explosion for large systems and extensibility to novel patterns. In this paper, a design pattern detection methodology is proposed that is based on similarity scoring between graph vertices. Due to the nature of the underlying graph algorithm, this approach has the ability to also recognize patterns that are modified from their standard representation. Moreover, the approach exploits the fact that patterns reside in one or more inheritance hierarchies, reducing the size of the graphs to which the algorithm is applied. Finally, the algorithm does not rely on any pattern-specific heuristic, facilitating the extension to novel design structures. Evaluation on three open-source projects demonstrated the accuracy and the efficiency of the proposed method

Identification of Move Method Refactoring Opportunities

Placement of attributes/methods within classes in an object-oriented system is usually guided by conceptual criteria and aided by appropriate metrics. Moving state and behavior between classes can help reduce coupling and increase cohesion, but it is nontrivial to identify where such refactorings should be applied. In this paper, we propose a methodology for the identification of Move Method refactoring opportunities that constitute a way for solving many common feature envy bad smells. An algorithm that employs the notion of distance between system entities (attributes/methods) and classes extracts a list of behavior-preserving refactorings based on the examination of a set of preconditions. In practice, a software system may exhibit such problems in many different places. Therefore, our approach measures the effect of all refactoring suggestions based on a novel entity placement metric that quantifies how well entities have been placed in system classes. The proposed methodology can be regarded as a semi-automatic approach since the designer will eventually decide whether a suggested refactoring should be applied or not based on conceptual or other design quality criteria. The evaluation of the proposed approach has been performed considering qualitative, metric, conceptual, and efficiency aspects of the suggested refactorings in a number of open-source projects.

Identification of extract method refactoring opportunities for the decomposition of methods

The extraction of a code fragment into a separate method is one of the most widely performed refactoring activities, since it allows the decomposition of large and complex methods and can be used in combination with other code transformations for fixing a variety of design problems. Despite the significance of Extract Method refactoring towards code quality improvement, there is limited support for the identification of code fragments with distinct functionality that could be extracted into new methods. The goal of our approach is to automatically identify Extract Method refactoring opportunities which are related with the complete computation of a given variable (complete computation slice) and the statements affecting the state of a given object (object state slice). Moreover, a set of rules regarding the preservation of existing dependences is proposed that exclude refactoring opportunities corresponding to slices whose extraction could possibly cause a change in program behavior. The proposed approach has been evaluated regarding its ability to capture slices of code implementing a distinct functionality, its ability to resolve existing design flaws, its impact on the cohesion of the decomposed and extracted methods, and its ability to preserve program behavior. Moreover, precision and recall have been computed employing the refactoring opportunities found by independent evaluators in software that they developed as a golden set.

An Empirical Study on Web Service Evolution

The service-oriented architecture paradigm prescribes the development of systems through the composition of services, i.e., network-accessible components, specified by (and invoked through) their WSDL interface descriptions. Systems thus developed need to be aware of changes in, and evolve with, their constituent services. Therefore, accurate recognition of changes in the WSDL specification of a service is an essential functionality in the context of the software lifecycle of service-oriented systems. In this work, we present the results of an empirical study on WSDL evolution analysis. In the first part, we empirically study whether VTracker, our algorithm for XML differencing, can precisely recognize changes in WSDL documents by applying it to the task of comparing 18 versions of the Amazon EC2 web service. Second, we analyze the changes that occurred between the subsequent versions of various web-services and discuss their potential effects on the maintainability of service systems relying on them.

JDeodorant: Identification and Removal of Feature Envy Bad Smells

In this demonstration we present an Eclipse plug-in that identifies feature envy bad smells in Java projects and resolves them by applying the appropriate move method refactorings. The main contribution is the ability to pre-evaluate the impact of all possible move refactorings on design quality and apply the most effective one.

Architectural Risk Analysis of Software Systems Based on Security Patterns

The importance of software security has been profound, since most attacks to software systems are based on vulnerabilities caused by poorly designed and developed software. Furthermore, the enforcement of security in software systems at the design phase can reduce the high cost and effort associated with the introduction of security during implementation. For this purpose, security patterns that offer security at the architectural level have been proposed in analogy to the well-known design patterns. The main goal of this paper is to perform risk analysis of software systems based on the security patterns that they contain. The first step is to determine to what extent specific security patterns shield from known attacks. This information is fed to a mathematical model based on the fuzzy-set theory and fuzzy fault trees in order to compute the risk for each category of attacks. The whole process has been automated using a methodology that extracts the risk of a software system by reading the class diagram of the system under study.

Identification of Extract Method Refactoring Opportunities

Extract Method has been recognized as one of the most important refactorings, since it decomposes large methods and can be used in combination with other refactorings for fixing a variety of design problems. However, existing tools and methodologies support extraction of methods based on a set of statements selected by the user in the original method. The goal of the proposed methodology is to automatically identify Extract Method refactoring opportunities and present them as suggestions to the designer of an object-oriented system. The suggested refactorings adhere to three principles: the extracted code should contain the complete computation of a given variable declared in the original method, the behavior of the program should be preserved after the application of the refactoring, and the extracted code should not be excessively duplicated in the original method. The proposed approach is based on the union of static slices that result from the application of a block-based slicing technique. The soundness of the identified refactoring opportunities has been evaluated by an independent designer on the system that he developed.

Decomposing object-oriented class modules using an agglomerative clustering technique

Software can be considered a live entity, as it undergoes many alterations throughout its lifecycle. Furthermore, developers do not usually retain a good design in favor of adding new features, comply with requirements or meet deadlines. For these reasons, code can become rather complex and difficult to understand. More particularly in object-oriented systems, classes may become very large and less cohesive. In order to identify such problematic cases, existing approaches have proposed the use of cohesion metrics. However, while metrics can identify classes with low cohesion, they cannot identify new or independent concepts. Moreover, these methods require a lot of human interpretation to identify the respective design flaws. In this paper, we propose a class decomposition method using an agglomerative clustering algorithm based on the Jaccard distance between class members. Our methodology is able to identify new concepts and rank the solutions according to their impact on the design quality of the system. Finally, our method has been evaluated by two independent designers who were asked to comment on the suggestions produced by our technique on their projects. The designers provided feedback on the ability of the method to identify new concepts and improve the design quality of the system in terms of cohesion.

Identification of refactoring opportunities introducing polymorphism

Polymorphism is one of the most important features offered by object-oriented programming languages, since it allows to extend/modify the behavior of a class without altering its source code, in accordance to the Open/Closed Principle. However, there is a lack of methods and tools for the identification of places in the code of an existing system that could benefit from the employment of polymorphism. In this paper we propose a technique that extracts refactoring suggestions introducing polymorphism. The approach ensures the behavior preservation of the code and the applicability of the refactoring suggestions based on the examination of a set of preconditions.

Why we refactor? confessions of GitHub contributors

Refactoring is a widespread practice that helps developers to improve the maintainability and readability of their code. However, there is a limited number of studies empirically investigating the actual motivations behind specific refactoring operations applied by developers. To fill this gap, we monitored Java projects hosted on GitHub to detect recently applied refactorings, and asked the developers to explain the reasons behind their decision to refactor the code. By applying thematic analysis on the collected responses, we compiled a catalogue of 44 distinct motivations for 12 well-known refactoring types. We found that refactoring activity is mainly driven by changes in the requirements and much less by code smells. Extract Method is the most versatile refactoring operation serving 11 different purposes. Finally, we found evidence that the IDE used by the developers affects the adoption of automated refactoring tools.