Using FCA to Suggest Refactorings to Correct Design Defects

Design defects are poor design choices resulting in a hard-to- maintain software, hence their detection and correction are key steps of a\ud disciplined software process aimed at yielding high-quality software\ud artifacts. While modern structure- and metric-based techniques enable\ud precise detection of design defects, the correction of the discovered\ud defects, e.g., by means of refactorings, remains a manual, hence\ud error-prone, activity. As many of the refactorings amount to re-distributing\ud class members over a (possibly extended) set of classes, formal concept\ud analysis (FCA) has been successfully applied in the past as a formal\ud framework for refactoring exploration. Here we propose a novel approach\ud for defect removal in object-oriented programs that combines the\ud effectiveness of metrics with the theoretical strength of FCA. A\ud case study of a specific defect, the Blob, drawn from the\ud Azureus project illustrates our approach

Refactorings of Design Defects using Relational Concept Analysis

Software engineers often need to identify and correct design defects, ıe} recurring design problems that hinder development and maintenance\ud by making programs harder to comprehend and--or evolve. While detection\ud of design defects is an actively researched area, their correction---mainly\ud a manual and time-consuming activity --- is yet to be extensively\ud investigated for automation. In this paper, we propose an automated\ud approach for suggesting defect-correcting refactorings using relational\ud concept analysis (RCA). The added value of RCA consists in exploiting\ud the links between formal objects which abound in a software re-engineering\ud context. We validated our approach on instances of the <span class='textit'></span>Blob\ud design defect taken from four different open-source programs

Desarrollo de Guías de Búsqueda de Metodologías del Proceso de Reingeniería de Aplicaciones Orientadas a Objetos

En la actualidad, las organizaciones no solo se enfrentan con el problema de reemplazar sus sistemas de información, sino que deben mantener un control de sus sistemas legacy. El mantenimiento de estos sistemas puede implicar realizar cambios para adaptarlo a nuevos requerimientos o nuevas tecnologías o bien, mejorar la calidad de los mismos. Para un adecuado mantenimiento de un sistema, es necesario aplicar reingeniería, que es definida por Chikofsky como la “recuperación de información sobre el diseño de un programa existente y el uso de esta información para reestructurar o reconstruir el programa existente”. Este proceso tiene como objetivo el de conseguir una mayor facilidad de mantenimiento en el futuro (mantenimiento preventivo). En un proceso de reingeniería completo identificaremos dos etapas: Reverse Enginnering y Forward Engineering. Debido a la complejidad de cada una de las etapas, durante los ultimos 20 anos han surgido diferentes metodologías que pueden aplicarse para obtener un proceso de reingeniería con calidad. Entonces se hace necesario realizar una evaluacion del estado del arte actual y desarrollar una guía que ayude a un desarrollador a elegir una metodología adecuada para mantener su sistema, dependiendo en que etapa de la reingeniería se encuentre. El aporte de este trabajo es el desarrollo de una guía de lectura con estrategias de busqueda para asistir a los desarrolladores en la elección de las metodologías de reingeniería adecuadas, dependiendo en que etapa de la reingeniería se halla el sistema. Específicamente en nuestro caso de estudio, esta guía esta basada en el estado del arte actual de metodologías de reingeniería que utilicen el Analisis de Conceptos Formales (ACF), como herramienta de base. A su vez, utilizaremos tambien el ACF como una técnica de clasificación para construir la guía mencionada. Se elige esta metodología debido a las ventajas que la misma provee para agrupar elementos basados en sus propiedades. Esto resulta vital para identificar metodologías con características similares y comprender como funciona cada propuesta de las publicaciones analizadas en el estado del arte.Sociedad Argentina de Informática e Investigación Operativ

Eliminating Code Duplication in Cascading Style Sheets

Cascading Style Sheets (i.e., CSS) is the standard styling language, widely used for defining the presentation semantics of user interfaces for web, mobile and desktop applications. Despite its popularity, CSS has not received much attention from academia. Indeed, developing and maintaining CSS code is rather challenging, due to the inherent language design shortcomings, the interplay of CSS with other programming languages (e.g., HTML and JavaScript), the lack of empirically- evaluated coding best-practices, and immature tool support. As a result, the quality of CSS code bases is poor in many cases. In this thesis, we focus on one of the major issues found in CSS code bases, i.e., the duplicated code. In a large, representative dataset of CSS code, we found an average of 68% duplication in style declarations. To alleviate this, we devise techniques for refactoring CSS code (i.e., grouping style declarations into new style rules), or migrating CSS code to take advantage of the code abstraction features provided by CSS preprocessor languages (i.e., superset languages for CSS that augment it by adding extra features that facilitate code maintenance). Specifically for the migration transformations, we attempt to align the resulting code with manually-developed code, by relying on the knowledge gained by conducting an empirical study on the use of CSS preprocessors, which revealed the common coding practices of the developers who use CSS preprocessor languages. To guarantee the behavior preservation of the proposed transformations, we come up with a list of preconditions that should be met, and also describe a lightweight testing technique. By applying a large number of transformations on several web sites and web applications, it is shown that the transformations are indeed presentation-preserving, and can effectively reduce the amount of duplicated code in CSS

Generating a Catalog of Unanticipated Schemas in Class Hierarchies using Formal Concept Analysis

International audienceContext: Inheritance is the cornerstone of object-oriented development, supporting conceptual modeling, subtype polymorphism and software reuse. But inheritance can be used in subtle ways that make complex systems hard to understand and extend, due to the presence of implicit dependencies in the inheritance hierarchy. Objective: Although these dependencies often specify well-known schemas (i.e., recurrent design or coding patterns, such as hook and template methods), new unanticipated dependency schemas arise in practice, and can consequently be hard to recognize and detect. Thus, a developer making changes or extensions to an object-oriented system needs to understand these implicit contracts defined by the dependencies between a class and its subclasses, or risk that seemingly innocuous changes break them. Method: To tackle this problem, we have developed an approach based on Formal Concept Analysis. Our FoCARE methodology (Formal Concept Analysis based-Reverse Engineering) identifies undocumented hi- erarchical dependencies in a hierarchy by taking into account the existing structure and behavior of classes and subclasses. Results: We validate our approach by applying it to a large and non-trivial case study, yielding a catalog of Hierarchy Schemas, each one composed of a set of dependencies over methods and attributes in a class hierarchy. We show how the discovered dependency schemas can be used not only to identify good design practices, but also to expose bad smells in design, thereby helping developers in initial reengineering phases to develop a first mental model of a system. Although some of the identified schemas are already documented in existing literature, with our approach based on Formal Concept Analysis (FCA), we are also able to identify previously unidentified schemas

Towards automated restructuring of object oriented systems

The work introduces a method for diagnosing design flaws in object oriented systems, and finding meaningful refactorings to remove such flaws. The method is based on pairing up a structural pattern that is considered pathological (e.g. a code smell or anti-pattern) with a so called design context. The design context describes the design semantics associated to the pathological structure, and the desired strategic closure for that fragment. The process is tool supported and largely automated

A Mono- and Multi-objective Approach for Recommending Software Refactoring

Les systèmes logiciels sont devenus de plus en plus répondus et importants dans notre société. Ainsi, il y a un besoin constant de logiciels de haute qualité. Pour améliorer la qualité de logiciels, l’une des techniques les plus utilisées est le refactoring qui sert à améliorer la structure d'un programme tout en préservant son comportement externe. Le refactoring promet, s'il est appliqué convenablement, à améliorer la compréhensibilité, la maintenabilité et l'extensibilité du logiciel tout en améliorant la productivité des programmeurs. En général, le refactoring pourra s’appliquer au niveau de spécification, conception ou code. Cette thèse porte sur l'automatisation de processus de recommandation de refactoring, au niveau code, s’appliquant en deux étapes principales: 1) la détection des fragments de code qui devraient être améliorés (e.g., les défauts de conception), et 2) l'identification des solutions de refactoring à appliquer. Pour la première étape, nous traduisons des régularités qui peuvent être trouvés dans des exemples de défauts de conception. Nous utilisons un algorithme génétique pour générer automatiquement des règles de détection à partir des exemples de défauts. Pour la deuxième étape, nous introduisons une approche se basant sur une recherche heuristique. Le processus consiste à trouver la séquence optimale d'opérations de refactoring permettant d'améliorer la qualité du logiciel en minimisant le nombre de défauts tout en priorisant les instances les plus critiques. De plus, nous explorons d'autres objectifs à optimiser: le nombre de changements requis pour appliquer la solution de refactoring, la préservation de la sémantique, et la consistance avec l’historique de changements. Ainsi, réduire le nombre de changements permets de garder autant que possible avec la conception initiale. La préservation de la sémantique assure que le programme restructuré est sémantiquement cohérent. De plus, nous utilisons l'historique de changement pour suggérer de nouveaux refactorings dans des contextes similaires. En outre, nous introduisons une approche multi-objective pour améliorer les attributs de qualité du logiciel (la flexibilité, la maintenabilité, etc.), fixer les « mauvaises » pratiques de conception (défauts de conception), tout en introduisant les « bonnes » pratiques de conception (patrons de conception).Software systems have become prevalent and important in our society. There is a constant need for high-quality software. Hence, to improve software quality, one of the most-used techniques is the refactoring which improves design structure while preserving the external behavior. Refactoring has promised, if applied well, to improve software readability, maintainability and extendibility while increasing the speed at which programmers can write and maintain their code. In general, refactoring can be performed in various levels such as the requirement, design, or code level. In this thesis, we mainly focus on the source code level where automated refactoring recommendation can be performed through two main steps: 1) detection of code fragments that need to be improved/fixed (e.g., code-smells), and 2) identification of refactoring solutions to achieve this goal. For the code-smells identification step, we translate regularities that can be found in such code-smell examples into detection rules. To this end, we use genetic programming to automatically generate detection rules from examples of code-smells. For the refactoring identification step, a search-based approach is used. The process aims at finding the optimal sequence of refactoring operations that improve software quality by minimizing the number of detected code-smells while prioritizing the most critical ones. In addition, we explore other objectives to optimize using a multi-objective approach: the code changes needed to apply refactorings, semantics preservation, and the consistency with development change history. Hence, reducing code changes allows us to keep as much as possible the initial design. On the other hand, semantics preservation insures that the refactored program is semantically coherent, and that it models correctly the domain-semantics. Indeed, we use knowledge from historical code change to suggest new refactorings in similar contexts. Furthermore, we introduce a novel multi-objective approach to improve software quality attributes (i.e., flexibility, maintainability, etc.), fix “bad” design practices (i.e., code-smells) while promoting “good” design practices (i.e., design patterns)

Improving software quality using an ontology-based approach

Ensuring quality in software development is a challenging process. The concepts of anti-pattern and bad code smells utilize the knowledge of reoccurring problems to improve the quality of current and future software development. Anti-patterns describe recurring bad design solutions while bad code smells describe source code that is error-free but difficult to understand and maintain. Code refactoring aims to remove bad code smells without changing a program’s functionality while improving program quality. There are metrics-based tools to detect a few bad code smells from source code; however, the knowledge and understanding of these indicators of low quality software are still insufficient to resolve many of the problems they represent. Minimal research addresses the relationships between or among bad code smells, anti-patterns and refactoring. In this research, we present a new ontology, Ontology for Anti-patterns, Bad Code Smells and Refactoring (OABR), to define the concepts and their relation properties. Such an ontological infrastructure encourages a common understanding of these concepts among the software community and provides more concise definitions that help to avoid overlapping and inconsistent description. It utilizes reasoning capabilities associated with ontology to analyze the software development domain and offer new insights into the domain. Software quality issues such as understandability and maintainability can be improved by identifying and resolving anti-patterns associated with code smells as well as preventing bad code smells before coding begins

Effectively incorporating expert knowledge in automated software remodularisation

Remodularising the components of a software system is challenging: sound design principles (e.g., coupling and cohesion) need to be balanced against developer intuition of which entities conceptually belong together. Despite this, automated approaches to remodularisation tend to ignore domain knowledge, leading to results that can be nonsensical to developers. Nevertheless, suppling such knowledge is a potentially burdensome task to perform manually. A lot information may need to be specified, particularly for large systems. Addressing these concerns, we propose the SUMO (SUpervised reMOdularisation) approach. SUMO is a technique that aims to leverage a small subset of domain knowledge about a system to produce a remodularisation that will be acceptable to a developer. With SUMO, developers refine a modularisation by iteratively supplying corrections. These corrections constrain the type of remodularisation eventually required, enabling SUMO to dramatically reduce the solution space. This in turn reduces the amount of feedback the developer needs to supply. We perform a comprehensive systematic evaluation using 100 real world subject systems. Our results show that SUMO guarantees convergence on a target remodularisation with a tractable amount of user interaction

Mining Version Histories for Detecting Code Smells

Code smells are symptoms of poor design and implementation choices that may hinder code comprehension, and possibly increase change-and fault-proneness. While most of the detection techniques just rely on structural information, many code smells are intrinsically characterized by how code elements change over time. In this paper, we propose Historical Information for Smell deTection (HIST), an approach exploiting change history information to detect instances of five different code smells, namely Divergent Change, Shotgun Surgery, Parallel Inheritance, Blob, and Feature Envy. We evaluate HIST in two empirical studies. The first, conducted on 20 open source projects, aimed at assessing the accuracy of HIST in detecting instances of the code smells mentioned above. The results indicate that the precision of HIST ranges between 72 and 86 percent, and its recall ranges between 58 and 100 percent. Also, results of the first study indicate that HIST is able to identify code smells that cannot be identified by competitive approaches solely based on code analysis of a single system\u27s snapshot. Then, we conducted a second study aimed at investigating to what extent the code smells detected by HIST (and by competitive code analysis techniques) reflect developers\u27 perception of poor design and implementation choices. We involved 12 developers of four open source projects that recognized more than 75 percent of the code smell instances identified by HIST as actual design/implementation problems