Scheduling Refactoring Opportunities Using Computational Search

Maintaining a high-level code quality can be extremely expensive since time and monetary pressures force programmers to neglect improving the quality of their source code. Refactoring is an extremely important solution to reduce and manage the growing complexity of software systems. Developers often need to make trade-offs between code quality, available resources and delivering a product on time, and such management support is beyond the scope and capability of existing refactoring engines. The problem of finding the optimal sequence in which the refactoring opportunities, such as bad smells, should be ordered is rarely studied. Due to the large number of possible scheduling solutions to explore, software engineers cannot manually find an optimal sequence of refactoring opportunities that may reduce the effort and time required to efficiently improve the quality of software systems. In this paper, we use bi-level multi-objective optimization to the refactoring opportunities management problem. The upper level generates a population of solutions where each solution is defined as an ordered list of code smells to fix which maximize the benefits in terms of quality improvements and minimize the cost in terms of number of refactorings to apply. The lower level finds the best sequence of refactorings that fixes the maximum number of code smells with a minimum number of refactorings for each solution (code smells sequence) in the upper level. The statistical analysis of our experiments over 30 runs on 6 open source systems and 1 industrial project shows a significant reduction in effort and better improvements of quality when compared to state-of-art bad smells prioritization techniques. The manual evaluation performed by software engineers also confirms the relevance of our refactoring opportunities scheduling solutions.Master of ScienceComputer Science, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136063/1/Scheduling Refactoring Opportunities Using Computational Search.pd

Management Aspects of Software Clone Detection and Analysis

Copying a code fragment and reusing it by pasting with or without minor modifications is a common practice in software development for improved productivity. As a result, software systems often have similar segments of code, called software clones or code clones. Due to many reasons, unintentional clones may also appear in the source code without awareness of the developer. Studies report that significant fractions (5% to 50%) of the code in typical software systems are cloned. Although code cloning may increase initial productivity, it may cause fault propagation, inflate the code base and increase maintenance overhead. Thus, it is believed that code clones should be identified and carefully managed. This Ph.D. thesis contributes in clone management with techniques realized into tools and large-scale in-depth analyses of clones to inform clone management in devising effective techniques and strategies. To support proactive clone management, we have developed a clone detector as a plug-in to the Eclipse IDE. For clone detection, we used a hybrid approach that combines the strength of both parser-based and text-based techniques. To capture clones that are similar but not exact duplicates, we adopted a novel approach that applies a suffix-tree-based k-difference hybrid algorithm, borrowed from the area of computational biology. Instead of targeting all clones from the entire code base, our tool aids clone-aware development by allowing focused search for clones of any code fragment of the developer's interest. A good understanding on the code cloning phenomenon is a prerequisite to devise efficient clone management strategies. The second phase of the thesis includes large-scale empirical studies on the characteristics (e.g., proportion, types of similarity, change patterns) of code clones in evolving software systems. Applying statistical techniques, we also made fairly accurate forecast on the proportion of code clones in the future versions of software projects. The outcome of these studies expose useful insights into the characteristics of evolving clones and their management implications. Upon identification of the code clones, their management often necessitates careful refactoring, which is dealt with at the third phase of the thesis. Given a large number of clones, it is difficult to optimally decide what to refactor and what not, especially when there are dependencies among clones and the objective remains the minimization of refactoring efforts and risks while maximizing benefits. In this regard, we developed a novel clone refactoring scheduler that applies a constraint programming approach. We also introduced a novel effort model for the estimation of efforts needed to refactor clones in source code. We evaluated our clone detector, scheduler and effort model through comparative empirical studies and user studies. Finally, based on our experience and in-depth analysis of the present state of the art, we expose avenues for further research and development towards a versatile clone management system that we envision

Automated Improvement of Software Design by Search-Based Refactoring

Le coût de maintenance du logiciel est estimé à plus de 70% du coût total du système, en raison de nombreux facteurs, y compris les besoins des nouveaux utilisateurs, l’adoption de nouvelles technologies et la qualité des systèmes logiciels. De ces facteurs, la qualité est celle que nous pouvons contrôler et continuellement améliorer pour empêcher la dégradation de la performance et la réduction de l’efficacité (par exemple, la dégradation de la conception du logiciel). De plus, pour rester compétitive, l’industrie du logiciel a raccourci ses cycles de lancement afin de fournir de nouveaux produits et fonctionnalités plus rapidement, ce qui entraîne une pression accrue sur les équipes de développeurs et une accélération de l’évolution de la conception du système. Une façon d’empêcher la dégradation du logiciel est l’identification et la correction des anti-patrons qui sont des indicateurs de mauvaise qualité de conception. Pour améliorer la qualité de la conception et supprimer les anti-patrons, les développeurs effectuent de petites transformations préservant le comportement (c.-à-d., refactoring). Le refactoring manuel est coûteux, car il nécessite (1) d’identifier les entités de code qui doivent être refactorisées ; (2) générer des opérations de refactoring pour les classes identifiées à l’étape précédente ; (3) trouver le bon ordre d’application des refactorings générés, pour maximiser le bénéfice pour la qualité du code et minimiser les conflits. Ainsi, les chercheurs et les praticiens ont formulé le refactoring comme un problème d’optimisation et utilisent des techniques basées sur la recherche pour proposer des approches (semi) automatisées pour le résoudre. Dans cette thèse, je propose plusieurs méthodes pour résoudre les principaux problèmes des outils existants, afin d’aider les développeurs dans leurs activités de maintenance et d’assurance qualité. Ma thèse est qu’il est possible d’améliorer le refactoring automatisé en considérant de nouvelles dimensions : (1) le contexte de tâche du développeur pour prioriser le refactoring des classes pertinentes ; (2) l’effort du test pour réduire le coût des tests après le refactoring ; (3) l’identification de conflit entre opérations de refactoring afin de réduire le coût de refactoring ; et (4) l’efficacité énergétique pour améliorer la consommation d’énergie des applications mobiles après refactoring.----------ABSTRACT: Software maintenance cost is estimated to be more than 70% of the total cost of system, because of many factors, including new user’s requirements, the adoption of new technologies and the quality of software systems. From these factors, quality is the one that we can control and continually improved to prevent degradation of performance and reduction of effectiveness (a.k.a. design decay). Moreover, to stay competitive, the software industry has shortened its release cycles to deliver new products and features faster, which results in more pressure on developer teams and the acceleration of system’s design evolution. One way to prevent design decay is the identification and correction of anti-patterns which are indicators of poor design quality. To improve design quality and remove anti-patterns, developers perform small behavior-preserving transformations (a.k.a. refactoring). Manual refactoring is expensive, as it requires to (1) identify the code entities that need to be refactored; (2) generate refactoring operations for classes identified in the previous step; (3) find the correct order of application of the refactorings generated, to maximize the quality effect and to minimize conflicts. Hence, researchers and practitioners have formulated refactoring as an optimization problem and use search-based techniques to propose (semi)automated approaches to solve it. In this dissertation, we propose several approaches to tackle some of the major issues in existing refactoring tools, to assist developers in their maintenance and quality assurance activities

CAViT: a Consistency Maintenance Framework based on Transformation Contracts

Design by contract is a software correctness methodology for procedural and object-oriented software. It relies on logical assertions to detect implementation mistakes at run-time or to proof the absence thereof at compile-time. Design by contract has found a new application in model driven engineering, a methodology that aims to manage the complexity of frameworks by relying on models and transformations. A ``transformation contract\u27\u27 is a pair of constraints that together describe the effect of a transformation rule on the set of models contained in its transformation definition: the postcondition describes the model consistency state that the rule can establish provided that its precondition is satisfied. A transformation contract of a rule can be maintained automatically by calling the rule (1) as soon as the invariant corresponding to its postcondition is violated and (2) provided that its precondition is satisfied. Domain specific visual languages can facilitate the implementation of the actual transformation rules since they hide the complexity of graph transformation algorithms and standards for tool interoperability. In this talk, we describe CAViT: a framework that integrates a visual model transformation tool with a design by contract tool by relying on OMG standards such as UML, OCL and MOF

Intelligent Web Services Architecture Evolution Via An Automated Learning-Based Refactoring Framework

Architecture degradation can have fundamental impact on software quality and productivity, resulting in inability to support new features, increasing technical debt and leading to significant losses. While code-level refactoring is widely-studied and well supported by tools, architecture-level refactorings, such as repackaging to group related features into one component, or retrofitting files into patterns, remain to be expensive and risky. Serval domains, such as Web services, heavily depend on complex architectures to design and implement interface-level operations, provided by several companies such as FedEx, eBay, Google, Yahoo and PayPal, to the end-users. The objectives of this work are: (1) to advance our ability to support complex architecture refactoring by explicitly defining Web service anti-patterns at various levels of abstraction, (2) to enable complex refactorings by learning from user feedback and creating reusable/personalized refactoring strategies to augment intelligent designers’ interaction that will guide low-level refactoring automation with high-level abstractions, and (3) to enable intelligent architecture evolution by detecting, quantifying, prioritizing, fixing and predicting design technical debts. We proposed various approaches and tools based on intelligent computational search techniques for (a) predicting and detecting multi-level Web services antipatterns, (b) creating an interactive refactoring framework that integrates refactoring path recommendation, design-level human abstraction, and code-level refactoring automation with user feedback using interactive mutli-objective search, and (c) automatically learning reusable and personalized refactoring strategies for Web services by abstracting recurring refactoring patterns from Web service releases. Based on empirical validations performed on both large open source and industrial services from multiple providers (eBay, Amazon, FedEx and Yahoo), we found that the proposed approaches advance our understanding of the correlation and mutual impact between service antipatterns at different levels, revealing when, where and how architecture-level anti-patterns the quality of services. The interactive refactoring framework enables, based on several controlled experiments, human-based, domain-specific abstraction and high-level design to guide automated code-level atomic refactoring steps for services decompositions. The reusable refactoring strategy packages recurring refactoring activities into automatable units, improving refactoring path recommendation and further reducing time-consuming and error-prone human intervention.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/142810/1/Wang Final Dissertation.pdfDescription of Wang Final Dissertation.pdf : Dissertatio

State of Refactoring Adoption: Towards Better Understanding Developer Perception of Refactoring

Context: Refactoring is the art of improving the structural design of a software system without altering its external behavior. Today, refactoring has become a well-established and disciplined software engineering practice that has attracted a significant amount of research presuming that refactoring is primarily motivated by the need to improve system structures. However, recent studies have shown that developers may incorporate refactoring strategies in other development-related activities that go beyond improving the design especially with the emerging challenges in contemporary software engineering. Unfortunately, these studies are limited to developer interviews and a reduced set of projects. Objective: We aim at exploring how developers document their refactoring activities during the software life cycle. We call such activity Self-Affirmed Refactoring (SAR), which is an indication of the developer-related refactoring events in the commit messages. After that, we propose an approach to identify whether a commit describes developer-related refactoring events, to classify them according to the refactoring common quality improvement categories. To complement this goal, we aim to reveal insights into how reviewers develop a decision about accepting or rejecting a submitted refactoring request, what makes such review challenging, and how to the efficiency of refactoring code review. Method: Our empirically driven study follows a mixture of qualitative and quantitative methods. We text mine refactoring-related documentation, then we develop a refactoring taxonomy, and automatically classify a large set of commits containing refactoring activities, and identify, among the various quality models presented in the literature, the ones that are more in-line with the developer\u27s vision of quality optimization, when they explicitly mention that they are refactoring to improve them to obtain an enhanced understanding of the motivation behind refactoring. After that, we performed an industrial case study with professional developers at Xerox to study the motivations, documentation practices, challenges, verification, and implications of refactoring activities during code review. Result: We introduced SAR taxonomy on how developers document their refactoring strategies in commit messages and proposed a SAR model to automate the detection of refactoring. Our survey with code reviewers has revealed several difficulties related to understanding the refactoring intent and implications on the functional and non-functional aspects of the software. Conclusion: Our SAR taxonomy and model, can work in conjunction with refactoring detectors, to report any early inconsistency between refactoring types and their documentation and can serve as a solid background for various empirical investigations. In light of our findings of the industrial case study, we recommended a procedure to properly document refactoring activities, as part of our survey feedback

Advances in Computer Science and Engineering

The book Advances in Computer Science and Engineering constitutes the revised selection of 23 chapters written by scientists and researchers from all over the world. The chapters cover topics in the scientific fields of Applied Computing Techniques, Innovations in Mechanical Engineering, Electrical Engineering and Applications and Advances in Applied Modeling