Sequence Of Refactorings Research Articles

MORCoRA: Multi-Objective Refactoring Recommendation Considering Review Availability

Background: Search-based refactoring involves searching for a sequence of refactorings to achieve specific objectives. Although a typical objective is improving code quality, a different perspective is also required; the searched sequence must undergo review before being applied and may not be applied if the review fails or is postponed due to no proper reviewers. Aim: Therefore, it is essential to ensure that the searched sequence of refactorings can be reviewed promptly by reviewers who meet two criteria: (1) having enough expertise and (2) being free of heavy workload. The two criteria are regarded as the review availability of the refactoring sequence. Method: We propose MORCoRA, a multi-objective search-based technique that can search for code quality improvable, semantic preserved, and high review availability possessed refactoring sequences and corresponding proper reviewers. Results: We evaluate MORCoRA on six open-source repositories. The quantitative analysis reveals that MORCoRA can effectively recommend refactoring sequences that fit the requirements. The qualitative analysis demonstrates that the refactorings recommended by MORCoRA can enhance code quality and effectively address code smells. Furthermore, the recommended reviewers for those refactorings possess high expertise and are available to review. Conclusions: We recommend that refactoring recommenders consider both the impact on quality improvement and the developer resources required for review when recommending refactorings.

Towards a catalog of composite refactorings

AbstractCatalogs of refactoring have key importance in software maintenance and evolution, since developers rely on such documents to understand and perform refactoring operations. Furthermore, these catalogs constitute a reference guide for communication between practitioners since they standardize a common refactoring vocabulary. Fowler's book describes the most popular catalog of refactorings, which documents single and well‐known refactoring operations. However, sometimes, refactorings are composite transformations, that is, a sequence of refactorings is performed over a given program element. For example, a sequence of Extract Method operations (a single refactoring) can be performed over the same method, in one or in multiple commits, to simplify its implementation, therefore, leading to a Method Decomposition operation (a composite refactoring). In this paper, we propose and document a catalog with eight composite refactorings. We also implement a set of scripts to mine composite refactorings by preprocessing the results of refactoring detection tools. Using such scripts, we search for composites in a representative refactoring oracle with hundreds of confirmed single refactoring operations. Next, to complement this first study, we also search for composites in the full history of 10 well‐known open‐source projects. We characterize the detected composite refactorings, under dimensions such as size and location. We conclude by addressing the applications and implications of the proposed catalog.

Dependent or Not: Detecting and Understanding Collections of Refactorings

Refactoring is a program transformation to improve the internal structure of a program while preserving its external behavior. Developers frequently apply multiple refactorings that depend on each other to achieve goals such as improving code reusability. Although manually applying a sequence of dependent refactorings is a common practice, existing refactoring recommendation tools treat refactorings in isolation without revealing the dependencies among them to developers. One reason is that these relationships among refactorings are poorly understood. Current approaches treat refactoring recommendations as a strictly ordered sequence limiting developers’ ability to understand, validate, and apply recommended refactorings. To address this gap, this paper describes a theory for reasoning about collections of refactorings through defining an ordering dependency relation among refactorings and organizing collection of refactorings as a set of refactoring graphs. We propose an algorithm for identifying refactoring dependencies and illustrate these concepts with a tool for visualizing such refactoring dependencies and refactoring graphs. Our validation results demonstrate that 43% of the 1,457,873 recommended refactorings from 9,595 projects that we studied are part of dependent refactoring graphs. Furthermore, refactorings are not only commonly involved in dependent relations, but also when applied, dependent refactoring graphs improve all of the quality attribute metrics in our experiments more than individual refactorings.

X-SBR: On the Use of the History of Refactorings for Explainable Search-Based Refactoring and Intelligent Change Operators

Refactoring is widely adopted nowadays in industry to restructure the code and meet high quality while preserving the external behavior. Many of the existing refactoring tools and research are based on search-based techniques to find relevant recommendations by finding trade-offs between different quality attributes. While these techniques show promising results on open-source and industry projects, they lack explanations of the recommended changes which can impact their trustworthiness when adopted in practice by developers. Furthermore, most of the adopted search-based techniques are based on random population generation and random change operators (e.g., crossover and mutation). However, it is critical to understand which good refactoring patterns may exist when applying change operators to either keep them or exchange with other solutions rather than destroying them with random changes. In this paper, we propose knowledge-informed change operators and an improved seeding mechanism that we integrated in a multi-objective genetic algorithm. We also provide explanations for refactoring solutions. First, we generate association rules using the Apriori algorithm to find relationships between applied refactorings in previous commits, their locations, and their rationale (quality improvements). Then, we use these rules to 1) initialize the population, 2) improve the change operators and seeding mechanisms of the multi-objective search in order to preserve and exchange good patterns in the refactoring solutions, and 3) explain how a sequence of refactorings collaborate in order to improve the quality of the system (e.g., fitness functions). The validation on large open-source systems shows that X-SBR provides refactoring solutions of a better quality than those given by the state-of-the-art techniques in terms of reducing the invalid refactorings, improving the quality, and increasing trustworthiness of the developers in the suggested refactorings via the provided explanations.

Prioritization of model smell refactoring using a covariance matrix-based adaptive evolution algorithm

The refactoring process enhances the software design by modifying the structure of design parts impaired with model smells without altering the overall software behavior. However, handling these smells without proper prioritization will not produce the anticipated effects. In this paper, we solve the prioritization of the model smell refactoring using a multi-objective optimization (MOO) algorithm called the multi-objective (MO) covariance matrix adaptation evolution strategy (MO CMA-ES). Our formulation relies on the refactoring of unified modeling language (UML) class diagrams to mitigate the negative effect of design smells. We treat the prioritization problem as a real-valued MOO where we propose novel data encoding procedures. We use two conflicting objectives, quality, and maintainability , to balance the refactoring. We first build a new solution representation that guarantees smell fixing and eliminates the rejection limitation. Furthermore, we suggest a custom mapping scheme to properly encode real-valued quantities using unique representations. For performance evaluation purposes, we developed a large custom dataset with more than 30,000 class records, using seven popular open-source software projects. A novel relative coverage metric is proposed to mitigate the limitations associated with the standard coverage. For benchmarking purposes, we also consider an improved version of the nondominated sorting genetic algorithm (NSGA-II(. The reported performance scores confirm the superiority of the MO CMA-ES algorithm over NSGA-II. The former successfully identified the refactoring sequences that lead to the best improvements in software quality and maintainability while it is able to fix all identified design smells. These improvements are quantified in terms of hypervolume, coverage, spacing metrics, and execution time. The MO CMA-ES attained the highest average maximum quality score of 1149 while keeping the average maintainability at the lowest score of 13.8. In all experiment settings, the proposed solution leads to longer refactoring sequences at no additional computational cost.

Identifying the Optimal Refactoring Dependencies Using Heuristic Search Algorithms to Maximize Maintainability

Bad smells represent imperfection in the design of the software system and trigger the urge to refactor the source code. The quality of object-oriented software has always been a major concern for the developer team and refactoring techniques help them to focus on this aspect by transforming the code in a way such that the behavior of the software can be preserved. Rigorous research has been done in this field to improve the quality of the software using various techniques. But, one of the issues still remains unsettled, i.e. the overhead effort to refactor the code in order to yield the maximum maintainability value. In this paper, a quantitative evaluation method has been proposed to improve the maintainability value by identifying the most optimum refactoring dependencies in advance with the help of various meta-heuristic algorithms, including A*, AO*, Hill-Climbing and Greedy approaches. A comparison has been done between the maintainability values of the software used, before and after applying the proposed methodology. The results of this study show that the Greedy algorithm is the most promising algorithm amongst all the algorithms in determining the most optimum refactoring sequence resulting in 18.56% and 9.90% improvements in the maintainability values of jTDS and ArtOfIllusion projects, respectively. Further, this study would be beneficial for the software maintenance team as refactoring sequences will be available beforehand, thereby helping the team in maintaining the software with much ease to enhance the maintainability of the software. The proposed methodology will help the maintenance team to focus on a limited portion of the software due to prioritization of the classes, in turn helping them in completing their work within the budget and time constraints.

A Brief Review on Multi-objective Software Refactoring and a New Method for Its Recommendation

Software refactoring is a commonly accepted means of improving the software quality without affecting its observable behaviour. It has gained significant attention from both academia and software industry. Therefore, numerous approaches have been proposed to automate refactoring that consider software quality maximization as their prime objective. However, this objective is not enough to generate good and efficient refactoring sequences as refactoring also involves several other uncertainties related to smell severity, history of applied refactoring activities and class severity. To address these concerns, we propose a multi-objective optimization technique to generate refactoring solutions that maximize the (1) software quality, (2) use of smell severity and (3) consistency with class importance. To this end, we provide a brief review on multi-objective search-based software refactoring and use a multi-objective spotted hyena optimizer (MOSHO) to obtain the best compromise between these three objectives. The proposed approach is evaluated on five open source datasets and its performance is compared with five different well-known state-of-the-art meta-heuristic and non-meta-heuristic approaches. The experimental results exhibit that the refactoring solutions provided by MOSHO are significantly better than other algorithms when class importance and code smell severity scores are used.

Assessment of optimum refactoring sequence to improve the software quality of object-oriented software

Software Maintenance is the most expensive phase of Software Development Lifecycle (SDLC) and therefore requires maximum effort and time. Any modification in the source code also introduces some bad smell which may be harmful for the design of the source code. These bad smells get accumulated with every phase of the SDLC if not corrected timely, therefore, they need to be removed as early as possible with the help of refactoring which improves the internal attributes of the software without affecting the external attributes. In order to complete the software under time and budget constraints, authors have attempted to determine the optimum sequence of refactoring technique (RT) in advance so that maintenance team can directly apply them in order to remove the bad smell which further enhances the quality of the software. Authors have applied different types of Hill- Climbing algorithms. Results shows that that the Steepest-Ascent Hill-Climbing algorithm is the most promising algorithm in finding the most optimum refactoring sequence with 20% improvement in the maintainability value.

UX-Painter: An Approach to Explore Interaction Fixes in the Browser

Usability and user interaction improvement is a central task in web development to guarantee the success of a web application. However, designers are barely able to keep up with the current development cycle because their practices are too costly, while interaction issues accumulate in applications that end-users keep suffering. In this work, we propose a method for designers to rapidly explore solutions through visual programming to the interaction problems of an application under development, even when it has been already deployed. The method is realized by a tool called UX-Painter, an exploratory tool for designers to apply quick fixes to interaction issues at the client-side of a web application without the need of any script programming knowledge. The palette of available fixes in UX-Painter are client-side web refactorings, i.e., changes to web page elements that solve specific user interaction problems without changing the underlying functionality. UX-Painter allows designers to quickly set up new versions of a web application by combining refactorings to create alternative designs for user testing or an inspection review. UX-Painter also provides the means to communicate design improvements, as a sequence of refactorings with clear semantics. We show the feedback provided by interviews with designers about UX-Painter's functionality and the results of a user test about its usability.

Investigating optimum refactoring sequence using hill-climbing algorithm

The surface indication of a deeper problem in the source code is bad smells that required to be removed as soon as possible. Not all smells are harmful, but if ignored, they automatically become faults which will further deteriorate the quality of the software. Therefore, this damage can be prevented with the help of refactoring that changes the internal attributes without affecting its external behavior. Every class of the source code contains numerous types of bad smells; therefore, more than one refactoring technique needs to be applied to clean the code. In this paper, we have tried to figure out the most optimum refactoring sequence that should be applied in a critically affected class of an open-source software (OSS), jTDS, to maximize its maintainability value with minimal effort. The results indicate improvement in maintainability value by 14.06% after the application of refactoring techniques. This study would be beneficial for the software maintenance team as this will encourage them to complete their work with much ease and will make them more efficient.

Prioritizing software refactoring opportunities using multi-objective optimization

Software refactoring aims to improve software quality and developer productivity. It has gained significant attention from both academia and software industry. Therefore, numerous approaches have been proposed to automate refactoring that consider software quality maximization as their prime objective. However, this objective is not enough to generate good and efficient refactoring sequences as refactoring also involves several other uncertainties related to smell severity, history of applied refactoring activities and class severity. To address these concerns, we propose a multi-objective optimization approach to generate refactoring solutions that maximize the (1) software quality, (2) use of smell severity and (3) consistency with class importance. To this end, we use Multi-objective Spotted Hyena Optimizer (MOSHO) to obtain the best compromise between these three objectives. The proposed approach is evaluated on five open source datasets and its performance is compared with five different well-known state-of-the-art meta-heuristic and non-meta-heuristic approaches. The experimental results demonstrate that the refactoring solutions provided by MOSHO are significantly better than other algorithms when class importance and code smell severity scores are used.

Search-Based Cost-Effective Software Remodularization

Software modularization is a technique used to divide a software system into independent modules (packages) that are expected to be cohesive and loosely coupled. However, as software systems evolve over time to meet new requirements, their modularizations become complex and gradually loose their quality. Thus, it is challenging to automatically optimize the classes’ distribution in packages, also known as remodularization. To alleviate this issue, we introduce a new approach to optimize software modularization by moving classes to more suitable packages. In addition to improving design quality and preserving semantic coherence, our approach takes into consideration the refactoring effort as an objective in itself while optimizing software modularization. We adapt the Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) of Deb et al. to find the best sequence of refactorings that 1) maximize structural quality, 2) maximize semantic cohesiveness of packages (evaluated by a semantic measure based on WordNet), and 3) minimize the refactoring effort. We report the results of an evaluation of our approach using open-source projects, and we show that our proposal is able to produce a coherent and useful sequence of recommended refactorings both in terms of quality metrics and from the developer’s points of view.

Model refactoring by example: A multi‐objective search based software engineering approach

AbstractDeclarative rules are frequently used in model refactoring in order to detect refactoring opportunities and to apply the appropriate ones. However, a large number of rules is required to obtain a complete specification of refactoring opportunities. Companies usually have accumulated examples of refactorings from past maintenance experiences. Based on these observations, we consider the model refactoring problem as a multi objective problem by suggesting refactoring sequences that aim to maximize both structural and textual similarity between a given model (the model to be refactored) and a set of poorly designed models in the base of examples (models that have undergone some refactorings) and minimize the structural similarity between a given model and a set of well‐designed models in the base of examples (models that do not need any refactoring). To this end, we use the Non‐dominated Sorting Genetic Algorithm (NSGA‐II) to find a set of representative Pareto optimal solutions that present the best trade‐off between structural and textual similarities of models. The validation results, based on 8 real world models taken from open‐source projects, confirm the effectiveness of our approach, yielding refactoring recommendations with an average correctness of over 80%. In addition, our approach outperforms 5 of the state‐of‐the‐art refactoring approaches.

A genetic algorithm-based approach for automated refactoring of component-based software

Context: During its lifecycle, a software system undergoes repeated modifications to quickly fulfill new requirements, but its underlying design is not properly adjusted after each update. This leads to the emergence of bad smells. Refactoring provides a de facto behavior-preserving approach to eliminate these anomalies. However, manually determining and performing useful refactorings is a formidable challenge, as stated in the literature. Therefore, framing object-oriented automated refactoring as a search-based technique has been proposed. However, the literature shows that search-based refactoring of component-based software has not yet received proper attention.Objective: This paper presents a genetic algorithm-based approach for the automated refactoring of component-based software. This approach consists of detecting component-relevant bad smells and eliminating these bad smells by searching for the best sequence of refactorings using a genetic algorithm.Method: Our approach consists of four steps. The first step includes studying the literature related to component-relevant bad smells and formulating bad smell detection rules. The second step involves proposing a catalog of component-relevant refactorings. The third step consists of constructing a source code model by extracting facts from the source code of a component-based software. The final step seeks to identify the best sequence of refactorings to apply to reduce the presence of bad smells in the source code model using a genetic algorithm. The latter uses bad smell detection rules as a fitness function and the catalog of refactorings as a means to explore the search space.Results: As a case study, we conducted experiments on an unbiased set of four real-world component-based applications. The results indicate that our approach is able to efficiently reduce the total number of bad smells by more than one half, which is an acceptable value compared to the recent literature. Moreover, we determined that our approach is also accurate in refactoring only components suffering from bad smells while leaving the remaining components untouched whenever possible. Furthermore, a statistical analysis shows that our genetic algorithm outperforms random search and local search in terms of efficiency and accuracy on almost all the systems investigated in this work.Conclusion: This paper presents a search-based approach for the automated refactoring of component-based software. To the best of our knowledge, our approach is the first to focus on component-based refactoring, whereas the state-of-the-art approaches focus only on object-oriented refactoring.

Exact search-space size for the refactoring scheduling problem

Ouni et al. “Maintainability defects detection and correction: a multi-objective approach” proposed a search-based approach for generating optimal refactoring sequences. They estimated the size of the search space for the refactoring scheduling problem using a formulation that is incorrect; the search space is estimated to be too much larger than it is. We provide in this paper the exact expression for computing the number of possible refactoring sequences of a software system. This could be useful for researchers and practitioners interested in developing new approaches to automate refactoring.

Multi-Criteria Code Refactoring Using Search-Based Software Engineering

One of the most widely used techniques to improve the quality of existing software systems is refactoring—the process of improving the design of existing code by changing its internal structure without altering its external behavior. While it is important to suggest refactorings that improve the quality and structure of the system, many other criteria are also important to consider, such as reducing the number of code changes, preserving the semantics of the software design and not only its behavior, and maintaining consistency with the previously applied refactorings. In this article, we propose a multi-objective search-based approach for automating the recommendation of refactorings. The process aims at finding the optimal sequence of refactorings that (i) improves the quality by minimizing the number of design defects, (ii) minimizes code changes required to fix those defects, (iii) preserves design semantics, and (iv) maximizes the consistency with the previously code changes. We evaluated the efficiency of our approach using a benchmark of six open-source systems, 11 different types of refactorings (move method, move field, pull up method, pull up field, push down method, push down field, inline class, move class, extract class, extract method, and extract interface) and six commonly occurring design defect types (blob, spaghetti code, functional decomposition, data class, shotgun surgery, and feature envy) through an empirical study conducted with experts. In addition, we performed an industrial validation of our technique, with 10 software engineers, on a large project provided by our industrial partner. We found that the proposed refactorings succeed in preserving the design coherence of the code, with an acceptable level of code change score while reusing knowledge from recorded refactorings applied in the past to similar contexts.

On the use of developers’ context for automatic refactoring of software anti-patterns

A novel automated refactoring approach that takes into account task context.A large scale case study showing the effectiveness of the proposed approach in two scenarios: root-canal and floss refactoring.Evaluation of the design quality of refactored solutions using a state-of-the-art quality model QMOOD. Anti-patterns are poor solutions to design problems that make software systems hard to understand and extend. Entities involved in anti-patterns are reported to be consistently related to high change and fault rates. Refactorings, which are behavior preserving changes are often performed to remove anti-patterns from software systems. Developers are advised to interleave refactoring activities with their regular coding tasks to remove anti-patterns, and consequently improve software design quality. However, because the number of anti-patterns in a software system can be very large, and their interactions can require a solution in a set of conflicting objectives, the process of manual refactoring can be overwhelming. To automate this process, previous works have modeled anti-patterns refactoring as a batch process where a program provides a solution for the total number of classes in a system, and the developer has to examine a long list of refactorings, which is not feasible in most situations. Moreover, these proposed solutions often require that developers modify classes on which they never worked before (i.e., classes on which they have little or no knowledge). To improve on these limitations, this paper proposes an automated refactoring approach, ReCon (Refactoring approach based on task Context), that leverages information about a developers task (i.e., the list of code entities relevant to the developers task) and metaheuristics techniques to compute the best sequence of refactorings that affects only entities in the developers context. We mine 1705 task contexts (collected using the Eclipse plug-in Mylyn) and 1013 code snapshots from three open-source software projects (Mylyn, PDE, Eclipse Platform) to assess the performance of our proposed approach. Results show that ReCon can remove more than 50% of anti-patterns in a software system, using fewer resources than the traditional approaches from the literature.

A robust multi-objective approach to balance severity and importance of refactoring opportunities

Refactoring large systems involves several sources of uncertainty related to the severity levels of code smells to be corrected and the importance of the classes in which the smells are located. Both severity and importance of identified refactoring opportunities (e.g. code smells) are difficult to estimate. In fact, due to the dynamic nature of software development, these values cannot be accurately determined in practice, leading to refactoring sequences that lack robustness. In addition, some code fragments can contain severe quality issues but they are not playing an important role in the system. To address this problem, we introduced a multi-objective robust model, based on NSGA-II, for the software refactoring problem that tries to find the best trade-off between three objectives to maximize: quality improvements, severity and importance of refactoring opportunities to be fixed. We evaluated our approach using 8 open source systems and one industrial project, and demonstrated that it is significantly better than state-of-the-art refactoring approaches in terms of robustness in all the experiments based on a variety of real-world scenarios. Our suggested refactoring solutions were found to be comparable in terms of quality to those suggested by existing approaches, better prioritization of refactoring opportunities and to carry an acceptable robustness price.

Improving multi-objective code-smells correction using development history

One of the widely used techniques to improve the quality of software systems is refactoring. Software refactoring improves the internal structure of the system while preserving its external behavior. These two concerns drive the existing approaches to refactoring automation. However, recent studies demonstrated that these concerns are not enough to produce correct and consistent refactoring solutions. In addition to quality improvement and behavior preservation, studies consider, among others, construct semantics preservation and minimization of changes. From another perspective, development history was proven as a powerful source of knowledge in many maintenance tasks. Still, development history is not widely explored in the context of automated software refactoring. In this paper, we use the development history collected from existing software projects to propose new refactoring solutions taking into account context similarity with situations seen in the past. We propose a multi-objective optimization-based approach to find good refactoring sequences that (1) minimize the number of code-smells, and (2) maximize the use of development history while (3) preserving the construct semantics. To this end, we use the non-dominated sorting genetic algorithm (NSGA-II) to find the best trade-offs between these three objectives. We evaluate our approach using a benchmark composed of five medium and large-size open-source systems and four types of code-smells (Blob, spaghetti code, functional decomposition, and data class). Our experimental results show the effectiveness of our approach, compared to three different state-of-the-art approaches, with more than 85% of code-smells fixed and 86% of suggested refactorings semantically coherent when the change history is used.

Software rejuvenation via a multi-agent approach

Usually, development teams devote a huge amount of time and effort on maintaining existing software. Since many of these maintenance tasks are not planned, the software tends to degrade over time, causing side effects mainly on its non-functional requirements. This paper proposes the use of a multi-agent system in order to perform perfective maintenance tasks in a software product through refactorings. The software developer chooses the quality attribute that the agents should improve and the agents are able to autonomously search the code for opportunities to apply perfective maintenance, apply the perfective maintenance, and evaluate if the source code quality has been improved. Its main contributions are: (i) the refactorings are autonomously done by software agents during the idle development time; (ii) all changes are stored in isolated branches in order to facilitate the communication with the developers; (iii) the refactorings are applied only when the program semantics is preserved; (iv) the agents are able to learn the more suitable sequence of refactorings to improve a specific quality attribute; and (v) the approach can be extended with other metrics and refactorings. This paper also presents a set of experimental studies that provide evidences of the benefits of our approach for software rejuvenation.