Learning Effective Changes for Software Projects

The primary motivation of much of software analytics is decision making. How to make these decisions? Should one make decisions based on lessons that arise from within a particular project? Or should one generate these decisions from across multiple projects? This work is an attempt to answer these questions. Our work was motivated by a realization that much of the current generation software analytics tools focus primarily on prediction. Indeed prediction is a useful task, but it is usually followed by "planning" about what actions need to be taken. This research seeks to address the planning task by seeking methods that support actionable analytics that offer clear guidance on what to do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set of actionable plans within and across projects. Each of these plans, if followed will improve the quality of the software project.Comment: 4 pages, 2 figures. This a submission for ASE 2017 Doctoral Symposiu

“Refactoring” Refactoring

Code refactoring’s primary impetus is to control technical debt, a metaphor for the cost in software development due to the extraneous human effort needed to resolve confusing, obfuscatory, or hastily-crafted program code. While these issues are often described as causing “bad smells,” not all bad smells emanate from the code itself. Some (often the most pungent and costly) originate in the formation, or expressions, of the antecedent intensions the software proposes to satisfy. Paying down such technical debt requires more than grammatical manipulations of the code. Rather, refactoring in this case must attend to a more inclusive perspective; particularly how stakeholders perceive the artifact; and their conception of quality – their appreciative system. First, this paper explores refactoring as an evolutionary design activity. Second, we generalize, or “refactor,” the concept of code refactoring, beyond changes to code structure, to improving design quality by incorporating the stakeholders’ experience of the artifact as it relates to their intensions. Third, we integrate this refactored refactoring as the organizing principle of design as a reflective practice. The objective is to improve the clarity, understandability, maintainability, and extensibility manifest in the stakeholder intensions, in the artifact, and in their interrelationship

Model refactoring using examples: a search‐based approach

One of the important challenges in model‐driven engineering is how to improve the quality of the models' design in order to help designers understand them. Refactoring represents an efficient technique to improve the quality of a design while preserving its behavior. Most of existing work on model refactoring relies on declarative rules to detect refactoring opportunities and to apply the appropriate refactorings. However, a complete specification of refactoring opportunities requires a huge number of rules. In this paper, we consider the refactoring mechanism as a combinatorial optimization problem where the goal is to find good refactoring suggestions starting from a small set of refactoring examples applied to similar contexts. Our approach, named model refactoring by example, takes as input an initial model to refactor, a set of structural metrics calculated on both initial model and models in the base of examples, and a base of refactoring examples extracted from different software systems and generates as output a sequence of refactorings. A solution is defined as a combination of refactoring operations that should maximize as much as possible the structural similarity based on metrics between the initial model and the models in the base of examples. A heuristic method is used to explore the space of possible refactoring solutions. To this end, we used and adapted a genetic algorithm as a global heuristic search. The validation results on different systems of real‐world models taken from open‐source projects confirm the effectiveness of our approach. Copyright © 2014 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108085/1/smr1644.pd

How Does Refactoring Impact Security When Improving Quality? A Security Aware Refactoring

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155871/1/RefactoringSecurityQMOOD__ICSE____Copy_.pd

A Refactoring Documentation Bot

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153325/1/TSE_DocumentationBot__Copy_deep_blue.pd

Model-based source code refactoring with interaction and visual cues

Refactoring source code involves the developer in a myriad of program detail that can obscure the design changes that they actually wish to bring about. On the other hand, refactoring a UML model of the code makes it easier to focus on the program design, but the burdensome task of applying the refactorings to the source code is left to the developer. In an attempt to obtain the advantages of both approaches, we propose a refactoring approach where the interaction with the developer takes place at the model level, but the actual refactoring occurs on the source code itself. We call this approach model-based source code refactoring and implement it in this paper using two tools: (1) Design-Imp enables the developer to use interactive search-based design exploration to create a UML-based desired design from an initial design extracted from the source code. It also provides visual cues to improve developer comprehension during the design-level refactoring process and to help the developer to discern between promising and poor refactoring solutions. (2) Code-Imp then refactors the original source so that it has the same functional behavior as the original program, and a design close to the one produced in the design exploration phase, that is, a design that has been confirmed as “desirable” by the developer. We evaluated our approach involving interaction and visual cues with industrial developers refactoring three Java projects, comparing it with an approach using interaction without visual cues and a fully automated approach. The results show that our approach yields refactoring sequences that are more acceptable both to the individual developer and to a set of independent expert refactoring evaluators. Furthermore, our approach removed more code smells and was evaluated very positively by the experiment participants.</p

MORE: A multi‐objective refactoring recommendation approach to introducing design patterns and fixing code smells

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137556/1/smr1843.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137556/2/smr1843_am.pd

Uma abordagem de otimização multiobjetivo para projeto arquitetural de linha de produto de software

Resumo: A indústria de software tem adotado a abordagem de Linha de Produto de Software (LPS) com o objetivo de aumentar o reúso de software e diminuir o tempo de produção e os custos de desenvolvimento dos produtos. Nessa abordagem, o principal artefato e a arquitetura de LPS (PLA - Product Line Architecture). No entanto, obter uma PLA modular, extensível e reusável e uma tarefa não trivial. O arquiteto pode se apoiar em métricas arquiteturais para definir e melhorar o projeto da PLA. Contudo, essa tarefa pode envolver vários fatores, muitas vezes conflitantes entre si, e encontrar o melhor trade-off entre as métricas utilizadas para avaliar o projeto transforma o projeto de PLA em uma tarefa que demanda grande esforço humano. Nesse contexto, o projeto de PLA pode ser formulado como um problema de otimização com varios fatores. Porém, elaborar um projeto que atenda a todos os fatores envolvidos pode ser mais difícil do que reconhecer um bom projeto. Problemas da Engenharia de Software similares a esse tem sido eficientemente resolvidos com algoritmos de busca em um campo de pesquisa conhecido como Engenharia de Software Baseada em Busca (SBSE - Search Based Software Engineering). Entretanto, as abordagens existentes utilizadas para otimizar arquiteturas de software nãao são apropriadas para projeto de PLAs, pois não consideram características específicas de LPS. Desse modo, este trabalho propõe uma abordagem de otimização multiobjetivo automatizada para avaliar e melhorar um projeto de PLA no que tange a modularização de características, estabilidade do projeto e extensibilidade de LPS. A abordagem proposta inclui: (a) um processo sistemático para conduzir a otimização de projeto de PLA por meio de algoritmos de busca; (b) um metamodelo que permite que esses algoritmos manipulem projetos de PLA; (c) novos operadores de busca para evoluir projetos de PLA em termos de modularização de características; e (d) um tratamento multiobjetivo para o problema de projeto de PLA. Esse tratamento multiobjetivo engloba métricas que indicam a modularização de características e a extensibilidade de LPS, além de métricas convencionais para medir princípios básicos de projeto como coesão e acoplamento. Ao final do processo de otimização, um conjunto de possíveis soluções de projeto de PLA que representam os melhores trade-off entre os objetivos otimizados e retornado. O arquiteto deve selecionar uma solução de acordo com as suas prioridades. A ferramenta OPLA-Tool foi desenvolvida para instanciar a abordagem usando algoritmos evolutivos multiobjetivos, os quais tem sido usados com sucesso na área de SBSE. Utilizando a OPLA-Tool, quatro estudos empíricos foram realizados com nove PLAs para avaliar: os operadores de busca propostos; o uso das métricas de LPS; e os algoritmos escolhidos. Em comparação às PLAs originais, os resultados mostraram que a abordagem proposta consegue gerar projetos mais estáveis, mais elegantes e com melhor modularização de características