Search-based detection of model level changes

Software models, defined as code abstractions, are iteratively refined, restructured, and evolved due to many reasons such as reflecting changes in requirements or modifying a design to enhance existing features. For understanding the evolution of a model a-posteriori, change detection approaches have been proposed for models. The majority of existing approaches are successful to detect atomic changes. However, composite changes, such as refactorings, are difficult to detect due to several possible combinations of atomic changes or eventually hidden changes in intermediate model versions that may be no longer available. Moreover, a multitude of refactoring sequences may be used to describe the same model evolution. In this paper, we propose a multi-objective approach to detect model changes as a sequence of refactorings. Our approach takes as input an exhaustive list of possible types of model refactoring operations, the initial model, and the revised model, and generates as output a list of refactoring applications representing a good compromise between the following two objectives (i) maximize the similarity between the expected revised model and the generated model after applying the refactoring sequence on the initial model, and (ii) minimize the number of atomic changes used to describe the evolution. In fact, minimizing the number of atomic changes can important since it is maybe easier for a designer to understand and analyze a sequence of refactorings (composite model changes) rather than an equivalent large list of atomic changes (Weissgerber and Diehl 2006). Due to the huge number of possible refactoring sequences, a metaheuristic search method is used to explore the space of possible solutions. To this end, we use the non-dominated sorting genetic algorithm (NSGA-II) to find the best trade-off between our two objectives. The paper reports on the results of an empirical study of our multi-objective model changes detection technique as applied on various versions of real-world models taken from open source projects and one industrial project. We compared our approach to the simple deterministic greedy algorithm, multi-objective particle swarm optimization (MOPSO), an existing mono-objective changes detection approach, and two model changes detection tools not based on computational search. The statistical test results provide evidence to support the claim that our proposal enables the generation of changes detection solutions with correctness higher than 85 %, in average, using a variety of real-world scenarios.     

On the use of many quality attributes for software refactoring: a many-objective search-based software engineering approach

Search-based software engineering (SBSE) solutions are still not scalable enough to handle high-dimensional objectives space. The majority of existing work treats software engineering problems from a single or bi-objective point of view, where the main goal is to maximize or minimize one or two objectives. However, most software engineering problems are naturally complex in which many conflicting objectives need to be optimized. Software refactoring is one of these problems involving finding a compromise between several quality attributes to improve the quality of the system while preserving the behavior. To this end, we propose a novel representation of the refactoring problem as a many-objective one where every quality attribute to improve is considered as an independent objective to be optimized. In our approach based on the recent NSGA-III algorithm, the refactoring solutions are evaluated using a set of 8 distinct objectives. We evaluated this approach on one industrial project and seven open source systems. We compared our findings to: several other many-objective techniques (IBEA, MOEA/D, GrEA, and DBEA-Eps), an existing multi-objective approach a mono-objective technique and an existing refactoring technique not based on heuristic search. Statistical analysis of our experiments over 31 runs shows the efficiency of our approach.     

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.     

Search-based refactoring for software maintenance

The high cost of software maintenance could be reduced by automatically improving the design of object-oriented programs without altering their behaviour. We have constructed a software tool capable of refactoring object-oriented programs to conform more closely to a given design quality model, by formulating the task as a search problem in the space of alternative designs. This novel approach is validated by two case studies, where programs are automatically refactored to increase flexibility, reusability and understandability as defined by a contemporary quality model. Both local and simulated annealing searches were found to be effective in this task.     

使用程序聚类技术的模块重构风险分析方法

随着软件系统的演化,其模块化结构会逐渐退化。软件重构是调整系统结构的重要手段,但哪些模块最需要重构却难以预测。提出了一种基于程序聚类技术的模块重构风险分析方法,该方法通过对目标系统进行结构聚类和语义聚类获得其参考模块化结构,然后比较现实模块化结构与参考模块化结构之间的差异,对程序模块的设计质量进行评价,识别出系统中重构风险较高的模块。实验以三个开源软件的演化历史作为研究对象,与传统的模块化度量方法进行了比较,结果表明采用所提方法获得的预测结果与实际重构活动有较好的吻合度,从而验证了该方法的有效性。     

An efficient approach to identify multiple and independent Move Method refactoring candidates

ContextApplication of a refactoring operation creates a new set of dependency in the revised design as well as a new set of further refactoring candidates. In the studies of stepwise refactoring recommendation approaches, applying one refactoring at a time has been used, but is inefficient because the identification of the best candidate in each iteration of refactoring identification process is computation-intensive. Therefore, it is desirable to accurately identify multiple and independent candidates to enhance efficiency of refactoring process.ObjectiveWe propose an automated approach to identify multiple refactorings that can be applied simultaneously to maximize the maintainability improvement of software. Our approach can attain the same degree of maintainability enhancement as the method of the refactoring identification of the single best one, but in fewer iterations (lower computation cost).MethodThe concept of maximal independent set (MIS) enables us to identify multiple refactoring operations that can be applied simultaneously. Each MIS contains a group of refactoring candidates that neither affect (i.e., enable or disable) nor influence maintainability on each other. Refactoring effect delta table quantifies the degree of maintainability improvement each elementary candidate. For each iteration of the refactoring identification process, multiple refactorings that best improve maintainability are selected among sets of refactoring candidates (MISs).ResultsWe demonstrate the effectiveness and efficiency of the proposed approach by simulating the refactoring operations on several large-scale open source projects such as jEdit, Columba, and JGit. The results show that our proposed approach can improve maintainability by the same degree or to a better extent than the competing method, choosing one refactoring candidate at a time, in a significantly smaller number of iterations. Thus, applying multiple refactorings at a time is both effective and efficient.ConclusionOur proposed approach helps improve the maintainability as well as the productivity of refactoring identification.     

A test case refactoring approach for pattern-based software development

In the current trend, Extreme Programing methodology is widely adopted by small and medium-sized projects for dealing with rapidly or indefinite changing requirements. Test-first strategy and code refactoring are the important practices of Extreme Programing for rapid development and quality support. The test-first strategy emphasizes that test cases are designed before system implementation to keep the correctness of artifacts during software development; whereas refactoring is the removal of “bad smell” code for improving quality without changing its semantics. However, the test-first strategy may conflict with code refactoring in the sense that the original test cases may be broken or inefficient for testing programs, which are revised by code refactoring. In general, the developers revise the test cases manually since it is not complicated. However, when the developers perform a pattern-based refactoring to improve the quality, the effort of revising the test cases is much more than that in simple code refactoring. In our observation, a pattern-based refactoring is composed of many simple and atomic code refactorings. If we have the composition relationship and the mapping rules between code refactoring and test case refactoring, we may infer a test case revision guideline in pattern-based refactoring. Based on this idea, in this research, we propose a four-phase approach to guide the construction of the test case refactoring for design patterns. We also introduce our approach by using some well-known design patterns and evaluate its feasibility by means of test coverage.     

Dynamic profiling-based approach to identifying cost-effective refactorings

ContextObject-oriented software undergoes continuous changes—changes often made without consideration of the software’s overall structure and design rationale. Hence, over time, the design quality of the software degrades causing software aging or software decay. Refactoring offers a means of restructuring software design to improve maintainability. In practice, efforts to invest in refactoring are restricted; therefore, the problem calls for a method for identifying cost-effective refactorings that efficiently improve maintainability. Cost-effectiveness of applied refactorings can be explained as maintainability improvement over invested refactoring effort (cost). For the system, the more cost-effective refactorings are applied, the greater maintainability would be improved. There have been several studies of supporting the arguments that changes are more prone to occur in the pieces of codes more frequently utilized by users; hence, applying refactorings in these parts would fast improve maintainability of software. For this reason, dynamic information is needed for identifying the entities involved in given scenarios/functions of a system, and within these entities, refactoring candidates need to be extracted.ObjectiveThis paper provides an automated approach to identifying cost-effective refactorings using dynamic information in object-oriented software.MethodTo perform cost-effective refactoring, refactoring candidates are extracted in a way that reduces dependencies; these are referred to as the dynamic information. The dynamic profiling technique is used to obtain the dependencies of entities based on dynamic method calls. Based on those dynamic dependencies, refactoring-candidate extraction rules are defined, and a maintainability evaluation function is established. Then, refactoring candidates are extracted and assessed using the defined rules and the evaluation function, respectively. The best refactoring (i.e., that which most improves maintainability) is selected from among refactoring candidates, then refactoring candidate extraction and assessment are re-performed to select the next refactoring, and the refactoring identification process is iterated until no more refactoring candidates for improving maintainability are found.ResultsWe evaluate our proposed approach in three open-source projects. The first results show that dynamic information is helpful in identifying cost-effective refactorings that fast improve maintainability; and, considering dynamic information in addition to static information provides even more opportunities to identify cost-effective refactorings. The second results show that dynamic information is helpful in extracting refactoring candidates in the classes where real changes had occurred; in addition, the results also offer the promising support for the contention that using dynamic information helps to extracting refactoring candidates from highly-ranked frequently changed classes.ConclusionOur proposed approach helps to identify cost-effective refactorings and supports an automated refactoring identification process.     

Identifying refactoring opportunities in object-oriented code: A systematic literature review

ContextIdentifying 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.ObjectiveThis paper provides a systematic literature review of existing studies identifying opportunities for code refactoring activities.MethodWe 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.ResultsThe 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.ConclusionsIt would be beneficial to perform further studies that consider more refactoring activities, involve researchers from industry, and use large-scale and industrial-based systems.     

基于约束规划的一类排序问题通用求解方法

多约束排序问题是生产调度中常遇到的问题,传统的优化模型及方法在适应约束改变等方面存在诸多不足。鉴于此,将多约束排序问题定义为约束满足问题,系统设计时将模型定义与求解算法分离,利用约束规划平台的基本约束构建特定领域的抽象约束库,形成可重构的多约束排序问题通用求解框架。应用时,根据问题需求不同可利用抽象约束库快速重构优化模型,针对重构的优化模型配置相应的求解算法即可实现问题求解。应用结果表明,提出的方法通用性强,可满足实际应用的要求。     

Rank-based refactoring decision support: two studies

Refactoring can result in code with improved maintainability and is considered a preventive maintenance activity. Managers of large projects need ways to decide where to apply scarce resources when performing refactoring. There is a lack of tools for supporting such decisions. We introduce a rank-based software measure-driven refactoring decision support approach to assist managers. The approach uses various static measures to develop a weighted rank, ranking classes or packages that need refactoring. We undertook two case studies to examine the effectiveness of the approach. Specifically, we wanted to see if the decision support tool yielded results similar to those of human analysts/managers and in less time so that it can be used to augment human decision making. In the first study, we found that our approach identified classes as needing refactoring that were also identified by humans. In the second study, a hierarchical approach was used to identify packages that had actually been refactored in 15 releases of the open source project Tomcat. We examined the overlap between the tool’s findings and the actual refactoring activities. The tool reached 100/86.7% recall on the package/class level. Though these studies were limited in size and scope, it appears that this approach is worthy of further examination.     

On fuzzy repetitions detection in documentation reuse

Increasing complexity of software documentation calls for additional requirements of document maintenance. Documentation reuse can make a considerable contribution to solve this problem. This paper presents a method for fuzzy repetitions search in software documentation that is based on software clone detection. The search results are used for document refactoring. This paper also presents Documentation Refactoring Toolkit implementing the proposed method and integrated with the DocLine project. The proposed approach is evaluated on documentation packages for a number of open-source projects: Linux Kernel, Zend Framework, Subversion, and DocBook.     

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.     

面向方面的自动化重构方法

提出一种将面向对象程序重构为面向方面范型的自动化方法。该方法分挖掘和重构两个阶段进行。第一阶段挖掘面向对象程序中潜在的横切关注点,第二阶段将横切关注点重构为方面。给出一个约13 000行代码的程序自动重构的评价结果。     

Exception handling refactorings: Directed by goals and driven by bug fixing

Exception handling design can improve robustness, which is an important quality attribute of software. However, exception handling design remains one of the less understood and considered parts in software development. In addition, like most software design problems, even if developers are requested to design with exception handling beforehand, it is very difficult to get the right design at the first shot. Therefore, improving exception handling design after software is constructed is necessary. This paper applies refactoring to incrementally improve exception handling design. We first establish four exception handling goals to stage the refactoring actions. Next, we introduce exception handling smells that hinder the achievement of the goals and propose exception handling refactorings to eliminate the smells. We suggest exception handling refactoring is best driven by bug fixing because it provides measurable quality improvement results that explicitly reveal the benefits of refactoring. We conduct a case study with the proposed refactorings on a real world banking application and provide a cost-effectiveness analysis. The result shows that our approach can effectively improve exception handling design, enhance software robustness, and save maintenance cost. Our approach simplifies the process of applying big exception handling refactoring by dividing the process into clearly defined intermediate milestones that are easily exercised and verified. The approach can be applied in general software development and in legacy system maintenance.     

一种基于模板的面向方面重构框架的研究

面向方面编程是一种新的编程范型,而面向方面重构则是当前面向方面软件开发中的一个研究热点。首先对面向方面重构进行了分类研究,然后引入基于角色的横切关注点重构方法,最后在此基础上提出一种基于模版的面向方面重构框架。     

一种基于类型传播分析的泛型实例重构方法

重构泛型实例有利于提高软件的复用性和类型安全,但现有重构方法的时间复杂度较高,不适用于即时持续的重构.分析了变量类型传播分析方法在重构中的不足,提出了一种改进的泛型变量类型传播分析方法.该方法通过引入一种可以描述复杂参数化类型关系的泛型类型传播图,以复制节点的方式实现泛型变量属性敏感的类型分析,并通过解决别名问题来提高分析的精度.实例研究表明,可以在与程序规模呈近似线性增长的时间复杂度内实施重构,取得了较满意的效果.     

Grammar-based generation of variable-selection heuristics for constraint satisfaction problems

We propose a grammar-based genetic programming framework that generates variable-selection heuristics for solving constraint satisfaction problems. This approach can be considered as a generation hyper-heuristic. A grammar to express heuristics is extracted from successful human-designed variable-selection heuristics. The search is performed on the derivation sequences of this grammar using a strongly typed genetic programming framework. The approach brings two innovations to grammar-based hyper-heuristics in this domain: the incorporation of if-then-else rules to the function set, and the implementation of overloaded functions capable of handling different input dimensionality. Moreover, the heuristic search space is explored using not only evolutionary search, but also two alternative simpler strategies, namely, iterated local search and parallel hill climbing. We tested our approach on synthetic and real-world instances. The newly generated heuristics have an improved performance when compared against human-designed heuristics. Our results suggest that the constrained search space imposed by the proposed grammar is the main factor in the generation of good heuristics. However, to generate more general heuristics, the composition of the training set and the search methodology played an important role. We found that increasing the variability of the training set improved the generality of the evolved heuristics, and the evolutionary search strategy produced slightly better results.     

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.     

What you like in design use to correct bad-smells

Over the past decades, many techniques and tools have been developed to support maintenance activities in order to improve software quality. One of the most efficient ones is software refactoring to eliminate bad-smells. A majority of existing work propose “standard” refactoring solutions that can be applied by hand for each kind of defect. However, it is difficult to prove or ensure the generality of these solutions to any kind of bad-smells or software codes. In this paper, we propose an approach to correct bad-smells using well-designed code. We use genetic algorithms to generate correction solutions defined as a combination of refactoring operations that maximize, as much as possible, the similarity between the corrected bad-smells and examples of well-designed code. We report the results of an evaluation of our approach using four open-source projects. Our proposal achieved high correction scores by fixing the majority of expected bad-smells.