Selection Of Design Patterns Research Articles

Implications of semi-supervised learning for design pattern selection

Implications of semi-supervised learning for design pattern selection

Software design pattern selection approaches: A systematic literature review

AbstractSoftware design patterns have a considerable impact on the software development life cycle. Design pattern (DP) is a reliable and reusable solution provided by software experts to obtain quality software design. However, due to the large number of design patterns, selecting the appropriate one is quite difficult. To overcome this difficulty, several approaches with different methods have been presented to suggest the appropriate DP. Despite conducting a number of studies that have explored some aspects of this field, such as design pattern selection tools and techniques, there is a need for a deeper understanding, analysis, classification, and thorough review of the design pattern selection process. So far, no systematic review of design pattern selection approaches is available. This paper aims to classify existing approaches, provide several criteria for comparing approaches, analyze each one, and identify and analyze the most important elements in this field, including open issues, data sets, and so on. The present investigation paper will help future research to employ the existing approaches taking into account the specification of each one and it also raises awareness about the approaches used in previous research and their potential limitations.

Design Diversity In Early Residential Complexes In Tehran (1950-1970); Lessons To Learn

During 1950 to 1970, some of the first residential complexes in Tehran were developed to accommodate low and middle-income people. Although they were among the first residential complexes designed and built in the country, various quantitative and qualitative measures had been considered in their design to satisfy the target residents’ needs and to meet the projects’ goals. What could be the reason for satisfaction, despite they were among the early experiences of residential complexes design in country? The main reason for the success of these complexes was due to perfect design considerations; i.e. correct selection of design patterns which are less frequent in recent housing complexes. Some of these design considerations include providing public, semi-public and private open spaces, paying attention to pedestrians, sustainability and respect for the surrounding urban and climatic environment. This paper reviews some examples of early housing complexes in Tehran, including Chaharsad-Dastgah, Narmak, Nazi-Abad, Shahr-Ara, Behjat-Abad, Farah, Kan and Chaharom-e-Aban, to help recognize past achievements and reapply them in designing new housing complexes.

DPWord2Vec: Better Representation of Design Patterns in Semantics

With the plain text descriptions of design patterns, developers could better learn and understand the definitions and usage scenarios of design patterns. To facilitate the automatic usage of these descriptions, e.g., recommending design patterns by free-text queries, design patterns and natural languages should be adequately associated. Existing studies usually use texts in design pattern books as the representations of design patterns to calculate similarities with the queries. However, this way is problematic. Lots of information of design patterns may be absent from design pattern books and many words would be out of vocabulary due to the content limitation of these books. To overcome these issues, a more comprehensive method should be constructed to estimate the relatedness between design patterns and natural language words. Motivated by Word2Vec, in this study, we propose DPWord2Vec that embeds design patterns and natural language words into vectors simultaneously. We first build a corpus containing more than 400 thousand documents extracted from design pattern books, Wikipedia, and Stack Overflow. Next, we redefine the concept of context window to associate design patterns with words. Then, the design pattern and word vector representations are learnt by leveraging an advanced word embedding method. The learnt design pattern and word vectors can be universally used in textual description based design pattern tasks. An evaluation shows that DPWord2Vec outperforms the baseline algorithms by 17.1%-96.5% in measuring the similarities between design patterns and words in terms of Spearman's rank correlation coefficient. Moreover, we adopt DPWord2Vec on two typical design pattern tasks. In the design pattern tag recommendation task, the DPWord2Vec based method outperforms two state-of-the-art algorithms by 6.6% and 32.7% respectively when considering Recall@10. In the design pattern selection task, DPWord2Vec improves the existing methods by 6.5%-70.7% in terms of MRR.

Annotation of Software Requirements Specification (SRS), Extractions of Nonfunctional Requirements, and Measurement of Their Tradeoff

Software requirement artifacts such as manuals request for proposals, and software requirements specification (SRS) are commonly focused on functional requirements. In most SRS files, nonfunctional requirements do not formally encoded or encoded as a whole, not for an individual design problem. Moreover, these nonfunctional requirements are intermingled with functional requirements. Therefore, these nonfunctional requirements need special attention to understand for successful project development. These nonfunctional requirements have an impact on each other and optimal tradeoff is required for balanced nonfunctional requirements set. NFRs have a negative and positive tradeoff with each other such as increase confidentiality, decrease the availability, and enhance authenticity. So, an optimum tradeoff among these design problem within a module is required to have better design decisions. Instead of considering all nonfunctional requirements, the NFRs that have mutual tradeoff is considered. In this paper, we devised a novel document annotation scheme for SRS and extracted nonfunctional requirements from these annotated artifacts. In the next step, we classified NFRs into two classes security triad and performance triad, and the cost is assumed constant for each NFR. From the design problem, the tradeoff ratio is calculated among NFRs associated with it. Then, the production possibility graph is plotted to estimate the optimum tradeoff ratio within the module. For estimation economic optimum from a set of NFR, iso-cost graphs by assuming the constant cost. Some hypothetical variations in cost are also examined using 3D iso-cost graph. The reason to measure these tradeoff is to make design decision more empirical and helpful for the selection of design patterns, especially secure design patterns.

Automated framework for classification and selection of software design patterns

Though, Unified Modeling Language (UML), Ontology, and Text categorization approaches have been used to automate the classification and selection of design pattern(s). However, there are certain issues such as time and effort for formal specification of new patterns, system context-awareness, and lack of knowledge which needs to be addressed. We propose a framework (i.e. Three-phase method) to discuss these issues, which can aid novice developers to organize and select the correct design pattern(s) for a given design problem in a systematic way. Subsequently, we propose an evaluation model to gauge the efficacy of the proposed framework via certain unsupervised learning techniques. We performed three case studies to describe the working procedure of the proposed framework in the context of three widely used design pattern catalogs and 103 design problems. We find the significant results of Fuzzy c-means and Partition Around Medoids (PAM) as compared to other unsupervised learning techniques. The promising results encourage the applicability of the proposed framework in terms of design patterns organization and selection with respect to a given design problem.

Implications of deep learning for the automation of design patterns organization

Though like other domains such as email filtering, web page classification, sentiment analysis, and author identification, the researchers have employed the text categorization approach to automate organization and selection of design patterns. However, there is a need to bridge the gap between the semantic relationship between design patterns (i.e. Documents) and the features which are used for the organization of design patterns. In this study, we propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN) which learns on the semantic representation of documents formulated in the form of feature vectors. We performed a case study in the context of a text categorization based automated system used for the classification and selection of software design patterns. In the case study, we focused on two main research objectives: 1) to empirically investigate the effect of feature sets constructed through the global filter-based feature selection methods besides the proposed approach, and 2) to evaluate the significant improvement in the classification decision (i.e. Pattern organization) of classifiers using the proposed approach. The adjustment of DBN parameters such as a number of hidden layers, nodes and iteration can aid a developer to construct a more illustrative feature set. The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set and improve the classifier’s performance in terms of organization of design patterns.

Realising the Potential of Design Patterns in Professional Learning

In the field of teaching and learning, education or learning design is currently receiving considerable attention in the literature and in discussions amongst academics and practitioners. Different methods for capturing and sharing designs that incorporate pedagogically sound practices have been researched and promoted, including design patterns. Despite the numerous documented benefits of using design patterns in education, they have failed to become widely established within the teaching and learning community. In this paper the authors discuss the genesis of design patterns in education and outline the action research journey which resulted in the selection of design patterns as their preferred method to document and share effective learning design practice. They also review the barriers to design pattern usage and consider the question of whether design patterns are still relevant in the current discipline of teaching and learning. Finally, they present a way forward for design patterns and their integration into the world of professional learning.

An expert system for the selection of software design patterns

Abstract: This paper describes the development of a prototype expert system for the selection of design patterns that are used in object‐oriented software. Design patterns provide one method of software reuse, which supports the goal of improved software development productivity. The prototype system represents an initial step towards providing an automated solution regarding the design pattern application problem, i.e. leading a designer to a suitable design pattern which is applicable to the problem at hand. The feasibility of using expert system technology to aid in the selection problem is demonstrated.

An approach for pattern oriented software development based on a design handbook

In many mature engineering disciplines, reuse of available design knowledge is helped by the presence of handbooks. These handbooks record the details of existing system components and help in the process of evaluating design alternatives while building new systems. In recent times, design patterns have been identified as fundamental components of an object oriented design. However, they are presented in a format that may not be best suited for systematic selection and use while evaluating design alternatives. This paper provides a procedure to construct a handbook based on design patterns. This handbook helps designers in methodical selection of design patterns. The construction of the handbook is based on the identification of a set of key attributes of a design pattern and quantification of these attributes using the principles of measurement theory. A new methodology for object oriented design which consults the handbook during the design process is also introduced. The proposed methodology along with the handbook helps in evaluating different design alternatives.