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Context: The use of controlled vocabularies (CVs) aims to increase the quality of the specifications of the software requirements, by producing well-written documentation to reduce both ambiguities and complexity. Many studies suggest that defects introduced at the requirements engineering (RE) phase have a negative impact, significantly higher than defects in the later stages of the software development lifecycle. However, the knowledge we have about the impact of using CVs, in specific RE activities, is very scarce. Objective: To identify and classify the type of CVs, and the impact they have on the requirements engineering phase of software development. Method: A systematic mapping study, collecting empirical evidence that is published up to July 2019. Results: This work identified 2348 papers published pertinent to CVs and RE, but only 90 primary published papers were chosen as relevant. The process of data extraction revealed that 79 studies reported the use of ontologies, whereas the remaining 11 were focused on taxonomies. The activities of RE with greater empirical support were those of specification (29 studies) and elicitation (28 studies). Seventeen different impacts of the CVs on the RE activities were classified and ranked, being the two most cited: guidance and understanding (38%), and automation and tool support (22%). Conclusions: The evolution of the last 10 years in the number of published papers shows that interest in the use of CVs remains high. The research community has a broad representation, distributed across the five continents. Most of the research focuses on the application of ontologies and taxonomies, whereas the use of thesauri and folksonomies is less reported. The evidence demonstrates the usefulness of the CVs in all RE activities, especially during elicitation and specification, helping developers understand, facilitating the automation process and identifying defects, conflicts and ambiguities in the requirements. Collaboration in research between academic and industrial contexts is low and should be promoted. [ABSTRACT FROM AUTHOR]

Context Numerous software design patterns have been introduced and cataloged either as a canonical or a variant solution to solve a design problem. The existing automatic techniques for design pattern(s) selection aid novice software developers to select the more appropriate design pattern(s) from the list of applicable patterns to solve a design problem in the designing phase of software development life cycle. Goal However, the existing automatic techniques are limited to the semi-formal specification, multi-class problem, an adequate sample size to make precise learning and individual classifier training in order to determine a candidate design pattern class and suggest more appropriate pattern(s). Method To address these issues, we exploit a text categorization based approach via Fuzzy c-means (unsupervised learning technique) that targets to present a systematic way to group the similar design patterns and suggest the appropriate design pattern(s) to developers related to the specification of a given design problem. We also propose an evaluation model to assess the effectiveness of the proposed approach in the context of several real design problems and design pattern collections. Subsequently, we also propose a new feature selection method Ensemble-IG to overcome the multi-class problem and improve the classification performance of the proposed approach. Results The promising experimental results suggest the applicability of the proposed approach in the domain of classification and selection of appropriate design patterns. Subsequently, we also observed the significant improvement in learning precision of the proposed approach through Ensemble-IG. Conclusion The proposed approach has four advantages as compared to previous work. First, the semi-formal specification of design patterns is not required as a prerequisite; second, the ground reality of class label assignment is not mandatory; third, lack of classifier’s training for each design pattern class and fourth, an adequate sample size is not required to make precise learning. [ABSTRACT FROM AUTHOR]

Global Software Development (GSD) has gained great attention during the past decade or so, and it is also being adopted by most of the software development organizations. The GSD approach is practiced by the software development organizations in China and a wide range of firms have established its operations in various Chinese cities. However, GSD practices becomes more challenging for the projects based on the agile concepts. The aim of this study is to develop a taxonomy of the factors that could positively impact the scaling process of agile methods in the Chinese GSD industry. The factors are identified by exploring the available literature and conducting industrial study with the Chinese agile and GSD practitioners. The finally reported factors are categorized, prioritized and developed their taxonomy using the multi-criterion decision making (MCDM) Fuzzy AHP approach. The given taxonomy is significant and progressive for GSD industry to assess and improve the scaling process of agile methods. • Explore the factors that could positively impact the agile scaling process in Chinese global software development (GSD) industry. • Develop the conceptual mapping of the identified factors. • Conceptual mapping is empirically validated. • The fuzzy AHP approach used to priorities the identified factors and the respective categories. • Develop the recommendation taxonomy of the factors based on the fuzzy AHP findings. [ABSTRACT FROM AUTHOR]

Increasingly, software development organizations are scaling agile practices in the global software development (GSD) environment in order to meet the requirements of the quickly changing and regularly developing business environment. The main objectives of this study are to investigate the key barriers and develop a prioritization-based taxonomy of the barriers for scaling agile development in the GSD environment. Total twenty-two barriers were extracted from the available literature and categorized into five categories, i.e. "human resources management", 'coordination", "technology", "project management", and "software methodology". In the next phase, the identified barriers and their categories were further validated using the questionnaire survey. In the final phase, fuzzy-AHP method, a multi-criterion decision making (MCDM) technique, was applied to prioritize and taxonomy of identified barriers and their related categories was designed. The contribution of this study is not limited to investigate the barriers, but it also provides the roadmap to tackle the issues related to the scaling agile methods in the GSD environment. • Identifying the barriers which negatively impact on the scaling programs of agile development in GSD environment. • Categorizing the identified barriers according to their types. • Validation of the identified barriers using the questionnaire survey. • Prioritizing the barriers using fuzzy AHP method by collecting responses of pair-wise comparison of factors. [ABSTRACT FROM AUTHOR]

The phenomena of global software development (GSD) face many challenges that are specifically associated with software process improvement (SPI). The main objective of this work is to develop the prioritization-based taxonomy of the SPI success factors using the fuzzy AHP approach. Total of twenty-one factors were extracted from the available literature that were further evaluated by conducting questionnaire survey with the SPI experts. In the second stage, multi-criterion decision making (MCDM) fuzzy AHP tool was used to prioritize and develop the taxonomy of the identified factors and their categories. The implications of fuzzy AHP approach are novel in this research area as it has been successfully used previously in different other domains e.g. electrical and electronics, supplier selection, agile software development and personnel selection. The contribution of this study is not only limited to the development of the factor's taxonomy, but also their proper prioritization by introducing the novel fuzzy AHP approach in the research field of GSD and SPI, which assist to remove the vagueness and uncertainty in the opinion of the process improvement experts. • Identifying the factors which positively affects the SPI practices in GSD environment. • Categorizing the identified factors according to their types. • Identifying the factors in the real world practices which positively affects the SPI practices in GSD environment. • Prioritizing the success factors using fuzzy AHP methods by collecting responses of pair-wise comparison of factors. [ABSTRACT FROM AUTHOR]