How changes affect software entropy: an empirical study

Context Software systems continuously change for various reasons, such as adding new features, fixing bugs, or refactoring. Changes may either increase the source code complexity and disorganization, or help to reducing it. Aim This paper empirically investigates the relationship of source code complexity and disorganization—measured using source code change entropy—with four factors, namely the presence of refactoring activities, the number of developers working on a source code file, the participation of classes in design patterns, and the different kinds of changes occurring on the system, classified in terms of their topics extracted from commit notes. Method We carried out an exploratory study on an interval of the life-time span of four open source systems, namely ArgoUML, Eclipse-JDT, Mozilla, and Samba, with the aim of analyzing the relationship between the source code change entropy and four factors: refactoring activities, number of contributors for a file, participation of classes in design patterns, and change topics. Results The study shows that (i) the change entropy decreases after refactoring, (ii) files changed by a higher number of developers tend to exhibit a higher change entropy than others, (iii) classes participating in certain design patterns exhibit a higher change entropy than others, and (iv) changes related to different topics exhibit different change entropy, for example bug fixings exhibit a limited change entropy while changes introducing new features exhibit a high change entropy. Conclusions Results provided in this paper indicate that the nature of changes (in particular changes related to refactorings), the software design, and the number of active developers are factors related to change entropy. Our findings contribute to understand the software aging phenomenon and are preliminary to identifying better ways to contrast it.     

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.     

Client-based cohesion metrics for Java programs

One purpose of software metrics is to measure the quality of programs. The results can be for example used to predict maintenance costs or improve code quality. An emerging view is that if software metrics are going to be used to improve quality, they must help in finding code that should be refactored. Often refactoring or applying a design pattern is related to the role of the class to be refactored. In client-based metrics, a project gives the class a context. These metrics measure how a class is used by other classes in the context. We present a new client-based metric LCIC (Lack of Coherence in Clients), which analyses if the class being measured has a coherent set of roles in the program. Interfaces represent the roles of classes. If a class does not have a coherent set of roles, it should be refactored, or a new interface should be defined for the class.We have implemented a tool for measuring the metric LCIC for Java projects in the Eclipse environment. We calculated LCIC values for classes of several open source projects. We compare these results with results of other related metrics, and inspect the measured classes to find out what kind of refactorings are needed. We also analyse the relation of different design patterns and refactorings to our metric. Our experiments reveal the usefulness of client-based metrics to improve the quality of code.     

Automated refactoring to the Null Object design pattern

ContextNull-checking conditionals are a straightforward solution against null dereferences. However, their frequent repetition is considered a sign of poor program design, since they introduce source code duplication and complexity that impacts code comprehension and maintenance. The Null Object design pattern enables the replacement of null-checking conditionals with polymorphic method invocations that are bound, at runtime, to either a real object or a Null Object.ObjectiveThis work proposes a novel method for automated refactoring to Null Object that eliminates null-checking conditionals associated with optional class fields, i.e., fields that are not initialized in all class instantiations and, thus, their usage needs to be guarded in order to avoid null dereferences.MethodWe introduce an algorithm for automated discovery of refactoring opportunities to Null Object. Moreover, we specify the source code transformation procedure and an extensive set of refactoring preconditions for safely refactoring an optional field and its associated null-checking conditionals to the Null Object design pattern. The method is implemented as an Eclipse plug-in and is evaluated on a set of open source Java projects.ResultsSeveral refactoring candidates are discovered in the projects used in the evaluation and their refactoring lead to improvement of the cyclomatic complexity of the affected classes. The successful execution of the projects’ test suites, on their refactored versions, provides empirical evidence on the soundness of the proposed source code transformation. Runtime performance results highlight the potential for applying our method to a wide range of project sizes.ConclusionOur method automates the elimination of null-checking conditionals through refactoring to the Null Object design pattern. It contributes to improvement of the cyclomatic complexity of classes with optional fields. The runtime processing overhead of applying our method is limited and allows its integration to the programmer’s routine code analysis activities.     

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.     

Refactoring software packages via community detection in complex software networks

An intrinsic property of software in a real-world environment is its need to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality, making software maintenance a tough problem. Refactoring is regarded as an effective way to address this problem. Many refactoring approaches at the method and class level have been proposed. But the research on software refactoring at the package level is very little. This paper presents a novel approach to refactor the package structures of object oriented software. It uses software networks to represent classes and their dependencies. It proposes a constrained community detection algorithm to obtain the optimized community structures in software networks, which also correspond to the optimized package structures. And it finally provides a list of classes as refactoring candidates by comparing the optimized package structures with the real package structures. The empirical evaluation of the proposed approach has been performed in two open source Java projects, and the benefits of our approach are illustrated in comparison with the other three approaches.     

Identifying extract class refactoring opportunities for internetware

The quality of internetware software is significantly associated with class structure.As software evolves,changes often introduce many unrelated responsibilities to the same classes or distribute tightly-related methods in different classes.These changes make the classes difficult to understand and maintain.Extract class refactoring is an effective technique to improve the quality of software structure by decomposing unrelated methods in one class to create new classes or extracting tightly-related methods from different classes.In this paper,we propose a novel approach for class extraction from internetware source codes.This approach leverages a community structure detection technique to partition software into clusters and extracts classes from the resulting clusters.Our experimental results,which investigate the public well-known internetware PKUAS,indicate that:(1)the proposed approach is much faster than existing search-based clustering approaches(Hillclimbing and Genetic algorithm)and is thus applicable for large-scale internetware;(2)the proposed approach can identify meaningful class extractions for internetware;and(3)Extract Class refactoring candidates identified by the proposed approach significantly improve class cohesion of internetware.     

Identification of generalization refactoring opportunities

Generalization refactoring helps relate classes and share functions, including both interfaces and implementation, by inheritance. To apply generalization refactoring, developers should first identify potential generalization refactoring opportunities, i.e., software entities that might benefit from generalization refactoring. For non-trivial software systems, manual identification of these opportunities is challenging and time-consuming. However, to the best of our knowledge, no existing tools have been specifically designed for this task. As a result, people have to identify these opportunities manually or with the help of tools designed for other purposes, e.g., clone detectors. To this end, we propose a tool GenReferee (Generalization Referee) to identify potential refactoring opportunities according to conceptual relationship, implementation similarity, structural correspondence, and inheritance hierarchies. It was first calibrated on two non-trivial open source applications, and then evaluated on another three. Evaluation results suggest that the proposed approach is effective and efficient.     

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.     

Constructing models for predicting extract subclass refactoring opportunities using object-oriented quality metrics

ContextRefactoring is a maintenance task that refers to the process of restructuring software source code to enhance its quality without affecting its external behavior. Inspecting and analyzing the source code of the system under consideration to identify the classes in need of extract subclass refactoring (ESR) is a time consuming and costly process.ObjectiveThis paper explores the abilities of several quality metrics considered individually and in combination to predict the classes in need of ESR.MethodFor a given a class, this paper empirically investigates, using univariate logistic regression analysis, the abilities of 25 existing size, cohesion, and coupling metrics to predict whether the class is in need of restructuring by extracting a subclass from it. In addition, models of combined metrics based on multivariate logistic regression analysis were constructed and validated to predict the classes in need of ESR, and the best model is justifiably recommended. We explored the statistical relations between the values of the selected metrics and the decisions of the developers of six open source Java systems with respect to whether the classes require ESR.ResultsThe results indicate that there was a strong statistical relation between some of the quality metrics and the decision of whether ESR activity was required. From a statistical point of view, the recommended model of metrics has practical thresholds that lead to an outstanding classification of the classes into those that require ESR and those that do not.ConclusionThe proposed model can be applied to automatically predict the classes in need of ESR and present them as suggestions to developers working to enhance the system during the maintenance phase. In addition, the model is capable of ranking the classes of the system under consideration according to their degree of need of ESR.     

Metrics for measuring complexity and completeness for social goal models

Goal-oriented Requirements Engineering approaches have become popular in the Requirements Engineering community as they provide expressive modelling languages for requirements elicitation and analysis. However, as a common challenge, such approaches are still struggling when it comes to managing the accidental complexity of their models. Furthermore, those models might be incomplete, resulting in insufficient information for proper understanding and implementation. In this paper, we provide a set of metrics, which are formally specified and have tool support, to measure and analyse complexity and completeness of goal models, in particular social goal models (e.g. i^⁎). Concerning complexity, the aim is to identify refactoring opportunities to improve the modularity of those models, and consequently reduce their accidental complexity. With respect to completeness, the goal is to automatically detect model incompleteness. We evaluate these metrics by applying them to a set of well-known system models from industry and academia. Our results suggest refactoring opportunities in the evaluated models, and provide a timely feedback mechanism for requirements engineers on how close they are to completing their models.     

Automating feature model refactoring: A Model transformation approach

Context: Feature model is an appropriate and indispensable tool for modeling similarities and differences among products of the Software Product Line (SPL). It not only exposes the validity of the products’ configurations in an SPL but also changes in the course of time to support new requirements of the SPL. Modifications made on the feature model in the course of time raise a number of issues. Useless enlargements of the feature model, the existence of dead features, and violated constraints in the feature model are some of the key problems that make its maintenance difficult.Objective: The initial approach to dealing with the above-mentioned problems and improving maintainability of the feature model is refactoring. Refactoring modifies software artifacts in a way that their externally visible behavior does not change.Method: We introduce a method for defining refactoring rules and executing them on the feature model. We use the ATL model transformation language to define the refactoring rules. Moreover, we provide an Alloy model to check the feature model and the safety of the refactorings that are performed on it.Results: In this research, we propose a safe framework for refactoring a feature model. This framework enables users to perform automatic and semi-automatic refactoring on the feature model.Conclusions: Automated tool support for refactoring is a key issue for adopting approaches such as utilizing feature models and integrating them into the software development process of companies. In this work, we define some of the important refactoring rules on the feature model and provide tools that enable users to add new rules using the ATL M2M language. Our framework assesses the correctness of the refactorings using the Alloy language.     

Supporting requirements to code traceability through refactoring

In this paper, we hypothesize that the distorted traceability tracks of a software system can be systematically re-established through refactoring, a set of behavior-preserving transformations for keeping the system quality under control during evolution. To test our hypothesis, we conduct an experimental analysis using three requirements-to-code datasets from various application domains. Our objective is to assess the impact of various refactoring methods on the performance of automated tracing tools based on information retrieval. Results show that renaming inconsistently named code identifiers, using Rename Identifier refactoring, often leads to improvements in traceability. In contrast, removing code clones, using eXtract Method (XM) refactoring, is found to be detrimental. In addition, results show that moving misplaced code fragments, using Move Method refactoring, has no significant impact on trace link retrieval. We further evaluate Rename Identifier refactoring by comparing its performance with other strategies often used to overcome the vocabulary mismatch problem in software artifacts. In addition, we propose and evaluate various techniques to mitigate the negative impact of XM refactoring. An effective traceability sign analysis is also conducted to quantify the effect of these refactoring methods on the vocabulary structure of software systems.     

Major motivations for extract method refactorings: analysis based on interviews and change histories

Extract method is one of the most popular software refactorings. However, little work has been done to investigate or validate the major motivations for such refactorings. Digging into this issue might help researchers to improve tool support for extract method refactorings, e.g., proposing better tools to recommend refactoring opportunities, and to select fragments to be extracted. To this end, we conducted an interview with 25 developers, and our results suggest that current reuse, decomposition of long methods, clone resolution, and future reuse are the major motivations for extract method refactorings.We also validated the results by analyzing the refactoring history of seven open-source applications. Analysis results suggest that current reuse was the primary motivation for 56% of extract method refactorings, decomposition of methods was the primary motivation for 28% of extract method refactorings, and clone resolution was the primary motivation for 16% of extract method refactorings. These findings might suggest that recommending extract method opportunities by analyzing only the inner structure (e.g., complexity and length) of methods alone would miss many extract method opportunities. These findings also suggest that extract method refactorings are often driven by current and immediate reuse. Consequently, how to recognize or predict reuse requirements timely during software evolution may play a key role in the recommendation and automation of extract method refactorings. We also investigated the likelihood for the extracted methods to be reused in future, and our results suggest that such methods have a small chance Received April 2, 2015; accepted November 10, 2015 E-mail: Liuhui08@bit.edu.cn (12%) to be reused in future unless the extracted fragment could be reused immediately in software evolution and extracting such a fragment can resolve existing clones at the same time.     

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.     

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。     

基于粗粒度切片度量的重构定位方法

重构是提高代码质量的一项关键技术,传统的定位重构代码依赖开发者的观察和主观意识,效率较低。为此,将切片思想引入到重构定位中,提出一种基于切片度量的重构定位方法。该方法利用粗粒度切片度量软件系统中类和类之间的耦合度,并根据度量结果识别类间的“坏味道”,从而定位需要重构的类。实例研究表明,与其他方法相比,该方法能获得更优的性能。     

一种基于横切特征分析的软件体系结构自动重构方法

软件体系结构中的横切关注点增加了软件体系结构的复杂性,从而加剧了体系结构演化与维护的困难.这种设计问题可以通过体系结构层面的重构来进行改善.在已有的横切特征分析方法基础上,提出了一种面向横切特征分析的体系结构自动重构方法.该方法首先基于特征与构件之间的追踪关系分析横切特征,然后将与横切特征有直接追踪关系的构件从初始体系结构中提取出来,实现方面构件,完成体系结构重构.在面向方面体系结构描述语言AO-ADL基础上开发了相应的体系结构重构工具,并针对一个业务系统进行了体系结构重构实验.实验结果表明,该方法能有效地实现体系结构横切特征的自动化重构.     

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.     

An experimental search-based approach to cohesion metric evaluation

In spite of several decades of software metrics research and practice, there is little understanding of how software metrics relate to one another, nor is there any established methodology for comparing them. We propose a novel experimental technique, based on search-based refactoring, to ‘animate’ metrics and observe their behaviour in a practical setting. Our aim is to promote metrics to the level of active, opinionated objects that can be compared experimentally to uncover where they conflict, and to understand better the underlying cause of the conflict. Our experimental approaches include semi-random refactoring, refactoring for increased metric agreement/disagreement, refactoring to increase/decrease the gap between a pair of metrics, and targeted hypothesis testing. We apply our approach to five popular cohesion metrics using ten real-world Java systems, involving 330,000 lines of code and the application of over 78,000 refactorings. Our results demonstrate that cohesion metrics disagree with each other in a remarkable 55 % of cases, that Low-level Similarity-based Class Cohesion (LSCC) is the best representative of the set of metrics we investigate while Sensitive Class Cohesion (SCOM) is the least representative, and we discover several hitherto unknown differences between the examined metrics. We also use our approach to investigate the impact of including inheritance in a cohesion metric definition and find that doing so dramatically changes the metric.