Naouel Moha

DECOR: A Method for the Specification and Detection of Code and Design Smells

Code and design smells are poor solutions to recurring implementation and design problems. They may hinder the evolution of a system by making it hard for software engineers to carry out changes. We propose three contributions to the research field related to code and design smells: (1) DECOR, a method that embodies and defines all the steps necessary for the specification and detection of code and design smells, (2) DETEX, a detection technique that instantiates this method, and (3) an empirical validation in terms of precision and recall of DETEX. The originality of DETEX stems from the ability for software engineers to specify smells at a high level of abstraction using a consistent vocabulary and domain-specific language for automatically generating detection algorithms. Using DETEX, we specify four well-known design smells: the antipatterns Blob, Functional Decomposition, Spaghetti Code, and Swiss Army Knife, and their 15 underlying code smells, and we automatically generate their detection algorithms. We apply and validate the detection algorithms in terms of precision and recall on XERCES v2.7.0, and discuss the precision of these algorithms on 11 open-source systems.

Meta-model Pruning

Large and complex meta-models such as those of Uml and its profiles are growing due to modelling and inter-operability needs of numerous stakeholders. The complexity of such meta-models has led to coining of the term meta-muddle . Individual users often exercise only a small view of a meta-muddle for tasks ranging from model creation to construction of model transformations. What is the effective meta-model that represents this view? We present a flexible meta-model pruning algorithm and tool to extract effective meta-models from a meta-muddle. We use the notion of model typing for meta-models to verify that the algorithm generates a super-type of the large meta-model representing the meta-muddle. This implies that all programs written using the effective meta-model will work for the meta-muddle hence preserving backward compatibility. All instances of the effective meta-model are also instances of the meta-muddle. We illustrate how pruning the original Uml meta-model produces different effective meta-models.

Recommendation system for design patterns in software development: an DPR overview

Software maintenance can become monotonous and expensive due to ignorance and misapplication of appropriate design patterns during the early phases of design and development. To have a good and reusable system, designers and developers must be aware of large information set and many quality concerns, e.g., design patterns. Systems with correct design pattern may ensure easy maintenance and evolution. However, without assistance, designing and development of software systems following certain design patterns is difficult for engineers. Recommendation systems for software engineering can assist designers and developers with a wide range of activities including suggesting design patterns. With the help of pattern recommenders, designers can come up with a reusable design. We provide a Design Pattern Recommender (DPR) process overview for software design to suggest design patterns, based on a simple Goal-Question-Metric (GQM) approach. Our prototype provides two-fold solution. In the primary-level, DPR only proposes one or more design patterns for a problem context, and in the secondary level, for a initial set of design, DPR refactors models and suggests design patterns. Our preliminary evaluation shows that DPR has a good trade-off between accuracy and procedural complexity, comparing to other state-of-the-art approaches.

Refactorings of design defects using relational concept analysis

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

Reusable model transformations

Model transformations written for an input metamodel may often apply to other metamodels that share similar concepts. For example, a transformation written to refactor Java models can be applicable to refactoring UML class diagrams as both languages share concepts such as classes, methods, attributes, and inheritance. Deriving motivation from this example, we present an approach to make model transformations reusable such that they function correctly across several similar metamodels. Our approach relies on these principal steps: (1) We analyze a transformation to obtain an effective subset of used concepts. We prune the input metamodel of the transformation to obtain an effective input metamodel containing the effective subset. The effective input metamodel represents the true input domain of transformation. (2) We adapt a target input metamodel by weaving it with aspects such as properties derived from the effective input metamodel. This adaptation makes the target metamodel a subtype of the effective input metamodel. The subtype property ensures that the transformation can process models conforming to the target input metamodel without any change in the transformation itself. We validate our approach by adapting well known refactoring transformations (Encapsulate Field, Move Method, and Pull Up Method) written for an in-house domain-specific modeling language (DSML) to three different industry standard metamodels (Java, MOF, and UML).

Generic Model Refactorings

Many modeling languages share some common concepts and principles. For example, Java, MOF, and UML share some aspects of the concepts of classes, methods, attributes, and inheritance. However, model transformations such as refactorings specified for a given language cannot be readily reused for another language because their related metamodels may be structurally different. Our aim is to enable a flexible reuse of model transformations across various metamodels. Thus, in this paper, we present an approach allowing the specification of generic model transformations, in particular refactorings, so that they can be applied to different metamodels. Our approach relies on two mechanisms: (1) an adaptation based mainly on the weaving of aspects; (2) the notion of model typing, an extension of object typing in the model-oriented context. We validated our approach by performing some experiments that consisted of specifying three well known refactorings ( Encapsulate Field,Move Method, and Pull Up Method) and applying each of them onto three different metamodels (Java, MOF, and UML).

A domain analysis to specify design defects and generate detection algorithms

Quality experts often need to identify in software systems design defects, which are recurring design problems, that hinder development and maintenance. Consequently, several defect detection approaches and tools have been proposed in the literature. However, we are not aware of any approach that defines and reifies the process of generating detection algorithms from the existing textual descriptions of defects. In this paper, we introduce an approach to automate the generation of detection algorithms from specifications written using a domain-specific language. The domain-specific is defined from a thorough domain analysis. We specify several design defects, generate automatically detection algorithms using templates, and validate the generated detection algorithms in terms of precision and recall on Xerces v2.7.0, an open-source object-oriented system.

Specification and detection of SOA antipatterns

Service-oriented architecture (SOA) provides a collection of principles and methodologies for designing and developing service-based systems (SBSs). SBSs are composed of loosely-coupled, platform independent, and reusable functional units, i.e., services. Alternative technologies to implement SBSs are REST-style (Representational State Transfer), Service Component Architecture (SCA), SOAP-based Web service, and so on. However, SBSs cannot overcome some common software engineering challenges, e.g., evolution, to fit new user requirements or changes in execution contexts. All these changes may degrade the quality of design and quality of service of SBSs and may cause the presence of common bad practiced solutions -- antipatterns.

From a domain analysis to the specification and detection of code and design smells

Code and design smells are recurring design problems in software systems that must be identified to avoid their possible negative consequences on development and maintenance. Consequently, several smell detection approaches and tools have been proposed in the literature. However, so far, they allow the detection of predefined smells but the detection of new smells or smells adapted to the context of the analysed systems is possible only by implementing new detection algorithms manually. Moreover, previous approaches do not explain the transition from specifications of smells to their detection. Finally, the validation of the existing approaches and tools has been limited on few proprietary systems and on a reduced number of smells. In this paper, we introduce an approach to automate the generation of detection algorithms from specifications written using a domain-specific language. This language is defined from a thorough domain analysis. It allows the specification of smells using high-level domain-related abstractions. It allows the adaptation of the specifications of smells to the context of the analysed systems. We specify 10 smells, generate automatically their detection algorithms using templates, and validate the algorithms in terms of precision and recall on Xerces v2.7.0 and GanttProject v1.10.2, two open-source object-oriented systems. We also compare the detection results with those of a previous approach, iPlasma.

Selection of Composable Web Services Driven by User Requirements

Building a composite application based on Web services has become a real challenge regarding the large and diverse service space nowadays. Especially when considering the various functional and non-functional capabilities that Web services may afford and users may require. In this paper, we propose an approach for facilitating Web service selection according to user requirements. These requirements specify the needed functionality and expected QoS, as well as the composability between each pair of services. The originality of our approach is embodied in the use of Relational Concept Analysis (RCA), an extension of Formal Concept Analysis (FCA). Using RCA, we classify services by their calculated QoS levels and composability modes. We use a real case study of 901 services to show how to accomplish an efficient selection of services satisfying a specified set of functional and non-functional requirements.