Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Interactive Multi-Objective Refactoring via Decision and Objective Space Exploration

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162566/1/ICSE2020_Decision_Objective_Spaces_copy (2).pdfSEL

A Tutorial on Software Engineering Intelligence: Case Studies on Model-Driven Engineering

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153783/1/MODELS_Tutorial__SEI___Copy_.pd

Interactive Refactoring via Clustering-Based Multi-objective Search

http://deepblue.lib.umich.edu/bitstream/2027.42/153328/1/ASE2018_Clustering_The_Pareto_Optimal_Solutions__Copy_DeepBlue.pd

A User-aware Intelligent Refactoring for Discrete and Continuous Software Integration

Successful software products evolve through a process of continual change. However, this process may weaken the design of the software and make it unnecessarily complex, leading to significantly reduced productivity and increased fault-proneness. Refactoring improves the software design while preserving overall functionality and behavior, and is an important technique in managing the growing complexity of software systems. Most of the existing work on software refactoring uses either an entirely manual or a fully automated approach. Manual refactoring is time-consuming, error-prone and unsuitable for large-scale, radical refactoring. Furthermore, fully automated refactoring yields a static list of refactorings which, when applied, leads to a new and often hard to comprehend design. In addition, it is challenging to merge these refactorings with other changes performed in parallel by developers. In this thesis, we propose a refactoring recommendation approach that dynamically adapts and interactively suggests refactorings to developers and takes their feedback into consideration. Our approach uses Non-dominated Sorting Genetic Algorithm (NSGAII) to find a set of good refactoring solutions that improve software quality while minimizing the deviation from the initial design. These refactoring solutions are then analyzed to extract interesting common features between them such as the frequently occurring refactorings in the best non-dominated solutions. We combined our interactive approach and unsupervised learning to reduce the developer’s interaction effort when refactoring a system. The unsupervised learning algorithm clusters the different trade-off solutions, called the Pareto front, to guide the developers in selecting their region of interests and reduce the number of refactoring options to explore. To reduce the interaction effort, we propose an approach to convert multi-objective search into a mono-objective one after interacting with the developer to identify a good refactoring solution based on their preferences. Since developers may want to focus on specific code locations, the ”Decision Space” is also important. Therefore, our interactive approach enables developers to pinpoint their preference simultaneously in the objective (quality metrics) and decision (code location) spaces. Due to an urgent need for refactoring tools that can support continuous integration and some recent development processes such as DevOps that are based on rapid releases, we propose, for the first time, an intelligent software refactoring bot, called RefBot. Our bot continuously monitors the software repository and find the best sequence of refactorings to fix the quality issues in Continous Integration/Continous Development (CI/CD) environments as a set of pull-requests generated after mining previous code changes to understand the profile of developers. We quantitatively and qualitatively evaluated the performance and effectiveness of our proposed approaches via a set of studies conducted with experienced developers who used our tools on both open source and industry projects.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154775/1/Vahid Alizadeh Final Dissertation.pdfDescription of Vahid Alizadeh Final Dissertation.pdf : Dissertatio

From Multi-Objective to Mono-Objective Refactoring via Developer's Knowledge Extraction

https://deepblue.lib.umich.edu/bitstream/2027.42/153327/1/From_Interactive_Multi_Objective_to_Mono_Objective_Refactoring__Copy_deepBlue (1).pd

30 Years of Software Refactoring Research: A Systematic Literature Review

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155872/4/30YRefactoring.pd

30 Years of Software Refactoring Research:A Systematic Literature Review

Due to the growing complexity of software systems, there has been a dramatic increase and industry demand for tools and techniques on software refactoring in the last ten years, defined traditionally as a set of program transformations intended to improve the system design while preserving the behavior. Refactoring studies are expanded beyond code-level restructuring to be applied at different levels (architecture, model, requirements, etc.), adopted in many domains beyond the object-oriented paradigm (cloud computing, mobile, web, etc.), used in industrial settings and considered objectives beyond improving the design to include other non-functional requirements (e.g., improve performance, security, etc.). Thus, challenges to be addressed by refactoring work are, nowadays, beyond code transformation to include, but not limited to, scheduling the opportune time to carry refactoring, recommendations of specific refactoring activities, detection of refactoring opportunities, and testing the correctness of applied refactorings. Therefore, the refactoring research efforts are fragmented over several research communities, various domains, and objectives. To structure the field and existing research results, this paper provides a systematic literature review and analyzes the results of 3183 research papers on refactoring covering the last three decades to offer the most scalable and comprehensive literature review of existing refactoring research studies. Based on this survey, we created a taxonomy to classify the existing research, identified research trends, and highlighted gaps in the literature and avenues for further research.Comment: 23 page

Interactive Decision and Objective Space Exploration for Search Based Refactoring

http://deepblue.lib.umich.edu/bitstream/2027.42/170142/1/ICSE2020_Decision_Objective_Spaces__Copy_.pdfSEL

Commits Analysis for Software Refactoring Documentation and Recommendation

Software projects frequently evolve to meet new requirements and/or to fix bugs. While this evolution is critical, it may have a negative impact on the quality of the system. To improve the quality of software systems, the first step is “detection" of code antipatterns to be restructured which can be considered as “refactoring opportunities". The second step is the “prioritization" of code fragments to be refactored/fixed. The third step is “recommendation" of refactorings to fix the detected quality issues. The fourth step is “testing" the recommended refactorings to evaluate their correctness. The fifth step is the “documentation" of the applied refactorings. In this thesis, we addressed the above five steps: 1. We designed a bi-level multi-objective optimization approach to enable the generation of antipattern examples that can improve the efficiency of detection rules for bad quality designs. 2. Regarding refactoring recommendations, we first identify refactoring opportunities by analyzing developer commit messages and quality of changed files, then we distill this knowledge into usable context driven refactoring recommendations to complement static and dynamic analysis of code. 3. We proposed an interactive refactoring recommendation approach that enables developers to pinpoint their preferences simultaneously in the objective (quality metrics) and decision (code location) spaces. 4. We proposed a semi-automated refactoring documentation bot that helps developers to interactively check and validate the documentation of the refactorings and/or quality improvements at the file level for each opened pull-request before being reviewed or merged to the master 5. We performed interviews with and a survey of practitioners as well as a quantitative analysis of 1,193 commit messages containing refactorings to establish a refactoring documentation model as a set of components. 6. We formulated the recommendation of code reviewers as a multi-objective search problem to balance the conflicting objectives of expertise, availability, and history of collaborations. 7. We built a dataset composed of 50,000+ composite code changes pertaining to more than 7,000 open-source projects. Then, we proposed and evaluated a new deep learning technique to generate commit messages for composite code changes based on an attentional encoder-decoder with two encoders and BERT embeddings.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/169486/1/Soumaya Rebai final dissertation.pdfDescription of Soumaya Rebai final dissertation.pdf : Dissertatio