Language Independent Metric Support towards Refactoring Inference

Computing Research Repository, 2010

We present a novel approach to detect refactoring opportunities by measuring the participation of references between types in instances of patterns representing design flaws. This technique is validated using an experiment where we analyse a set of 95 open-source Java programs for instances of four patterns representing modularisation problems. It turns out that our algorithm can detect high impact refactorings opportunities - a small number of references such that the removal of those references removes the majority of patterns from the program.

2010

In the continual evolution of software applications should be ensured that these are high quality designed and programmed. But inevitably appear the defective code that call “code smell”. It is therefore important to be able to find such problems, and to correct it. Code refactoring is a process which solves “code smell” and to improve maintainability, performance, or to improve extensibility. The aim of this paper is to investigate and propose automatic – universal refactoring tool, which is detected in self-“code smell” and is independent of software systems programming languages. This tool uses a logic programming, which is a factual description of the conversion, and the rules – the refactoring of the program. It also aims to create a practical benefit of the automatic adjustments to the proposed tool is to be realized, and a demonstration of refactoring operation.

Journal of Software: Evolution and Process, 2019

Bad smells tend to have a negative impact on software by degrading its quality. It is beneficial to detect model smells to avoid their propagation to later stages of software development. The objective of this paper is to present the state-of-the-art research on techniques for detecting UML model bad smells. The detection techniques are compared and evaluated using a proposed evaluation framework. The framework consists of two parts. The first part of the framework compares the techniques in terms of the implemented approach, the investigated model, and the explored model smells, while the experimental design is explored in the second part of the framework. We found that the detection of bad smells in class and sequence diagrams is accomplished via design patterns, software metrics, and predefined rules, while model smells in use cases are detected using metrics and predefined rules. We also found that the class diagram is the most investigated UML model in the context of model smell detection, whereas there is a lack of work on other UML models. In addition, there is a scarcity of independent studies on sequence diagrams. Furthermore, the studies investigating class diagrams are mostly validated, whereas use case diagrams and sequence diagrams are rarely validated.

International Journal of System Assurance Engineering and Management, 2017

During development process software may encounter design flaws which are referred as ''code smell''. These code smells are potential faults that can be handled using suitable refactoring approach. Refactoring being an expensive process is a thought provocation task, and there is a need to identify the relationship between code smells and design metrics by considering expert's opinion. The approach adopted in this paper ranks the design metrics, according to their criticality and need using AHP. This method helps to identify refactoring approach that may be used to improve the code and needs to be implemented before the execution phase. The proposed approach is explained considering a case study of medium sized software.

Context: Identifying refactoring opportunities in object-oriented code is an important stage that precedes the actual refactoring process. Several techniques have been proposed in the literature to identify opportunities for various refactoring activities. Objective: This paper provides a systematic literature review of existing studies identifying opportunities for code refactoring activities. Method: We performed an automatic search of the relevant digital libraries for potentially relevant studies published through the end of 2013, performed pilot and author-based searches, and selected 47 primary studies (PSs) based on inclusion and exclusion criteria. The PSs were analyzed based on a number of criteria, including the refactoring activities, the approaches to refactoring opportunity identification, the empirical evaluation approaches, and the data sets used. Results: The results indicate that research in the area of identifying refactoring opportunities is highly active. Most of the studies have been performed by academic researchers using nonindustrial data sets. Extract Class and Move Method were found to be the most frequently considered refactoring activities. The results show that researchers use six primary existing approaches to identify refactoring opportunities and six approaches to empirically evaluate the identification techniques. Most of the systems used in the evaluation process were open-source, which helps to make the studies repeatable. However, a relatively high percentage of the data sets used in the empirical evaluations were small, which limits the generality of the results. Conclusions: It would be beneficial to perform further studies that consider more refactoring activities, involve researchers from industry, and use large-scale and industrial-based systems.

2012

Abstract A large number of software metrics have been proposed in the literature, but there is little understanding of how these metrics relate to one another. We propose a novel experimental technique, based on search-based refactoring, to assess software metrics and to explore relationships between them. Our goal is not to improve the program being refactored, but to assess the software metrics that guide the auto-mated refactoring through repeated refactoring experiments.

IEEE Access

The expenses associated with software maintenance and evolution constitute a significant portion, surpassing more than 80% of the overall costs involved in software development. Refactoring, a widely embraced technique, plays a crucial role in streamlining and minimizing maintenance activities and expenses. However, the effect of refactoring techniques on quality attributes presents inconsistent and conflicting findings, making it challenging for software developers to enhance software quality effectively. Additionally, the absence of a comprehensive framework further complicates the decision-making process for developers when selecting appropriate refactoring techniques aligned with specific design objectives. In light of these considerations, this research aims to introduce a novel framework for classifying refactoring techniques based on their measurable influence on internal quality attributes. Initially, an exploratory study was conducted to identify commonly employed refactoring techniques, followed by an experimental analysis involving five case studies to evaluate the effects of these techniques on internal quality attributes. Subsequently, the framework was constructed based on the outcomes of the exploratory and experimental studies, further reinforced by a multi-case analysis. Comprising three key components, namely the methodology for applying refactoring techniques, the Quality Model for Object-Oriented Design (QMOOD), and the classification scheme for refactoring techniques, this proposed framework serves as a valuable guideline for developers. By comprehending the effect of each refactoring technique on internal quality attributes, developers can make informed decisions and select suitable techniques to enhance specific aspects of their software. Consequently, this framework optimizes developers' time and effort by minimizing the need to weigh the pros and cons of different refactoring techniques, potentially leading to a reduction in maintenance activities and associated costs.

Tool support for refactoring is widespread nowadays. The most widely known IDEs include refactoring support, and many refactoring-specific tools are also available. Developers are aware of refactoring activities and they do refactor their applications even manually or in an assisted way. For the users of refactoring tools, the current state of the art is well documented in refactoring catalogs, where objectives, motivation, mechanisms, etc. are defined. There are also available collections of criteria to detect refactoring opportunities; compilations of guidelines to help decide when and how to apply refactorings. However, refactoring tool implementors can not only base their work on the documentation available in refactoring catalogs because they do not offer clear guidelines on how to build the tools to automate these refactorings. Implementing refactoring tools without any guidance, involves reasoning about which will be the better plan to implement refactoring operations in the tool, their complexity or their graphical interface design, etc. This paper introduces a refactoring characterization, and proposes how it can help refactoring tool implementors on making decisions.

Innovations in Computer Science and Engineering, 2018

This paper discusses refactoring, which is one of the techniques to keep software maintainable. However, refactoring itself will not bring the full benefits, if we do not understand when refactoring needs to be applied. To make it easier for a software developer to decide whether certain software needs refactoring or not, Fowler & Beck (Fowler & Beck 2000) give a list of bad code smells. Bad smells are signs of potential problems in code. Detecting and resolving bad smells, however, remain time-consuming for software engineers despite proposals on bad smell detection and refactoring tools. Numerous bad smells have been recognized, yet the sequences in which the detection and resolution of different kinds of bad smells are performed are rarely discussed because software engineers do not know how to optimize sequences or determine the benefits of an optimal sequence. To this end, we propose a detection and resolution sequence for different kinds of bad smells to simplify their detection and resolution. We highlight the necessity of managing bad smell resolution sequences with a motivating example, and recommend a suitable sequence for commonly occurring bad smells. We evaluate this recommendation on two nontrivial open source applications, and the evaluation results suggest that a significant reduction in effort ranging from 17.64 to 20 percent can be achieved when bad smells are detected and resolved using the proposed sequence.

2006

Software repositories are rich sources of information about the software development process. Mining the information stored in them has been shown to provide interesting insights into the history of the software development and evolution. Several different types of information have been extracted and analyzed from different points of view. However, these types of information have not been sufficiently cross-examined to understand how they might complement each other. In this paper, we present a systematic analysis of four aspects of the software repository of an open source project -source-code metrics, identifiers, return-on-investment estimates, and design differencing -to collect evidence about refactorings that may have happened during the project development.