Behind the Intent of Extract Method Refactoring: A Systematic Literature Review

Code refactoring is widely recognized as an essential software engineering practice to improve the understandability and maintainability of the source code. The Extract Method refactoring is considered as "Swiss army knife" of refactorings, as developers often apply it to improve their code quality. In recent years, several studies attempted to recommend Extract Method refactorings allowing the collection, analysis, and revelation of actionable data-driven insights about refactoring practices within software projects. In this paper, we aim at reviewing the current body of knowledge on existing Extract Method refactoring research and explore their limitations and potential improvement opportunities for future research efforts. Hence, researchers and practitioners begin to be aware of the state-of-the-art and identify new research opportunities in this context. We review the body of knowledge related to Extract Method refactoring in the form of a systematic literature review (SLR). After compiling an initial pool of 1,367 papers, we conducted a systematic selection and our final pool included 83 primary studies. We define three sets of research questions and systematically develop and refine a classification schema based on several criteria including their methodology, applicability, and degree of automation. The results construct a catalog of 83 Extract Method approaches indicating that several techniques have been proposed in the literature. Our results show that: (i) 38.6% of Extract Method refactoring studies primarily focus on addressing code clones; (ii) Several of the Extract Method tools incorporate the developer's involvement in the decision-making process when applying the method extraction, and (iii) the existing benchmarks are heterogeneous and do not contain the same type of information, making standardizing them for the purpose of benchmarking difficult

Detection and analysis of near-miss clone genealogies

It is believed that identical or similar code fragments in source code, also known as code clones, have an impact on software maintenance. A clone genealogy shows how a group of clone fragments evolve with the evolution of the associated software system, and thus may provide important insights on the maintenance implications of those clone fragments. Considering the importance of studying the evolution of code clones, many studies have been conducted on this topic. However, after a decade of active research, there has been a marked lack of progress in understanding the evolution of near-miss software clones, especially where statements have been added, deleted, or modified in the copied fragments. Given that there are a significant amount of near-miss clones in the software systems, we believe that without studying the evolution of near-miss clones, one cannot have a complete picture of the clone evolution. In this thesis, we have advanced the state-of-the-art in the evolution of clone research in the context of both exact and near-miss software clones. First, we performed a large-scale empirical study to extend the existing knowledge about the evolution of exact and renamed clones where identifiers have been modified in the copied fragments. Second, we have developed a framework, gCad that can automatically extract both exact and near-miss clone genealogies across multiple versions of a program and identify their change patterns reasonably fast while maintaining high precision and recall. Third, in order to gain a broader perspective of clone evolution, we extended gCad to calculate various evolutionary metrics, and performed an in-depth empirical study on the evolution of both exact and near-miss clones in six open source software systems of two different programming languages with respect to five research questions. We discovered several interesting evolutionary phenomena of near-miss clones which either contradict with previous findings or are new. Finally, we further improved gCad, and investigated a wide range of attributes and metrics derived from both the clones themselves and their evolution histories to identify certain attributes, which developers often use to remove clones in the real world. We believe that our new insights in the evolution of near-miss clones, and about how developers approach and remove duplication, will play an important role in understanding the maintenance implications of clones and will help design better clone management systems

A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges

Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.Comment: 49 pages, 10 figures, 6 table

Understanding the Evolution of Code Clones in Software Systems

Code cloning is a common practice in software development. However, code cloning has both positive aspects such as accelerating the development process and negative aspects such as causing code bloat. After a decade of active research, it is clear that removing all of the clones from a software system is not desirable. Therefore, it is better to manage clones than to remove them. A software system can have thousands of clones in it, which may serve multiple purposes. However, some of the clones may cause unwanted management difficulties and clones like these should be refactored. Failure to manage clones may cause inconsistencies in the code, which is prone to error. Managing thousands of clones manually would be a difficult task. A clone management system can help manage clones and find patterns of how clones evolve during the evolution of a software system. In this research, we propose a framework for constructing and visualizing clone genealogies with change patterns (e.g., inconsistent changes), bug information, developer information and several other important metrics in a software system. Based on the framework we design and build an interactive prototype for a multi-touch surface (e.g., an iPad). The prototype uses a variety of techniques to support understanding clone genealogies, including: identifying and providing a compact overview of the clone genealogies along with their key characteristics; providing interactive navigation of genealogies, cloned source code and the differences between clone fragments; providing the ability to filter and organize genealogies based on their properties; providing a feature for annotating clone fragments with comments to aid future review; and providing the ability to contact developers from within the system to find out more information about specific clones. To investigate the suitability of the framework and prototype for investigating and managing cloned code, we elicit feedback from practicing researchers and developers, and we conduct two empirical studies: a detailed investigation into the evolution of function clones and a detailed investigation into how clones contribute to bugs. In both empirical studies we are able to use the prototype to quickly investigate the cloned source code to gain insights into clone use. We believe that the clone management system and the findings will play an important role in future studies and in managing code clones in software systems

A Topic Modeling approach for Code Clone Detection

In this thesis work, the potential benefits of Latent Dirichlet Allocation (LDA) as a technique for code clone detection has been described. The objective is to propose a language-independent, effective, and scalable approach for identifying similar code fragments in relatively large software systems. The main assumption is that the latent topic structure of software artifacts gives an indication of the presence of code clones. It can be hypothesized that artifacts with similar topic distributions contain duplicated code fragments and to prove this hypothesis, an experimental investigation using multiple datasets from various application domains were conducted. In addition, CloneTM, an LDA-based working prototype for code clone detection was developed. Results showed that, if calibrated properly, topic modeling can deliver a satisfactory performance in capturing different types of code clones, showing particularity good performance in detecting Type III clones. CloneTM also achieved levels of performance comparable to already existing practical tools that adopt different clone detection strategies

A case study of refactoring large-scale industrial systems to efficiently improve source code quality

Refactoring source code has many benefits (e.g. improving maintainability, robustness and source code quality), but it takes time away from other implementation tasks, resulting in developers neglecting refactoring steps during the development process. But what happens when they know that the quality of their source code needs to be improved and they can get the extra time and money to refactor the code? What will they do? What will they consider the most important for improving source code quality? What sort of issues will they address first or last and how will they solve them? In our paper, we look for answers to these questions in a case study of refactoring large-scale industrial systems where developers participated in a project to improve the quality of their software systems. We collected empirical data of over a thousand refactoring patches for 5 systems with over 5 million lines of code in total, and we found that developers really optimized the refactoring process to significantly improve the quality of these systems. © 2014 Springer International Publishing

Improving the Unification of Software Clones using Tree and Graph Matching Algorithms

Code duplication is common in all kind of software systems and is one of the most troublesome hurdles in software maintenance and evolution activities. Even though these code clones are created for the reuse of some functionality, they usually go through several modifications after their initial introduction. This has a serious negative impact on the maintainability, comprehensibility, and evolution of software systems. Existing code duplication can be eliminated by extracting the common functionality into a single module. In the past, several techniques have been developed for the detection and management of software clones. However, the unification and refactoring of software clones is still a challenging problem, since the existing tools are mostly focused on clone detection and there is no tool to find particularly refactoring-oriented clones. The programmers need to manually understand the clones returned by the clone detection tools, decide whether they should be refactored, and finally perform their refactoring. This obvious gap between the clone detection tools and the clone analysis tools, makes the refactoring tedious and the programmers reluctant towards refactoring duplicate codes. In this thesis, an approach for the unification and refactoring of software clones that overcomes the limitations of previous approaches is presented. More specifically, the proposed technique is able to detect and parameterize non-trivial differences between the clones. Moreover, it can find a mapping between the statements of the clones that minimizes the number of differences. We have also defined preconditions in order to determine whether the duplicated code can be safely refactored to preserve the behavior of the existing code. We compared the proposed technique with a competitive clone refactoring tool and concluded that our approach is able to find a significantly larger number of refactorable clones