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The software industry has widely adopted global software development (GSD) to gain economic benefits. Organizations that engage in GSD face various challenges, the majority being associated with requirements change management (RCM). The key motive of this study is to develop a requirement change management and implementation maturity model (SRCMIMM) for the GSD industry that could help the practitioners to assess and manage their RCM activities. A systematic literature review and questionnaire survey approach are used to identify and validate the critical success factors (CSFs), critical challenges (CCHs), and the related best practices of the RCM process. The investigated CSFs and CCHs are classified into five maturity levels based on the concepts of the existing maturity models in other domains, practitioners' feedback, and academic research. Every maturity level comprises different CSFs and CCHs that can help assess and manage a firm's RCM capability. To evaluate the effectiveness of the proposed model, four case studies are conducted in different GSD firms. The SRCMIMM has been developed to assist GSD organizations in improving their RCM process in efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

The recommendation systems plays an important role in today's life as it assist in reliable selection of common utilities. The code recommendation system is being used by the code databases (GitHub, source frog etc.) aiming to recommend the more appropriate code to the users. There are several factors that could negatively impact the performance of code recommendation systems (CRS). This study aims to empirically explore the challenges that could have critical impact on the performance of the CRS. Using systematic literature review and questionnaire survey approaches, 19 challenges were identified. Secondly, the investigated challenges were further prioritized using fuzzy-AHP analysis. The identification of challenges, their categorization and the fuzzy-AHP analysis provides the prioritization-based taxonomy of explored challenges. The study findings will assist the real-world industry experts and to academic researchers to improve and develop the new techniques for the improvement of CRS. [ABSTRACT FROM AUTHOR]

In recent years, smartphone devices are becoming progressively popular across a diverse range of users. However, user diversity creates challenges in smartphone application (app) development. The diversity of users is often ignored by designers and developers due to the absence of requirements. Owing to this, many smartphone users face usability issues. Despite that, no dedicated platform found that guide smartphone app designers and developers regarding human universality. The aim of this research is to explore the requirements of diverse users in smartphone apps and provide usability guidelines. The objectives of this research are achieved by following two scientific approaches. The human diversity requirements are located by conducting usability tests that investigated the requirements in the form of usability issues. The systematic literature review (SLR) process is followed in order to resolve the discovered usability issues. Both approaches resulted in a list of usability issues and guidelines. The usability tests returned 27 problems while the SLR came with a comprehensive set of universal usability guidelines that were grouped into eleven categories. The study concluded with some major outcomes. The results show evidence of critical usability problems that must be addressed during the design and development of smartphone apps. Moreover, the study also revealed that people with disabilities were three times severely affected by usability problems in such apps than people of different ages and their needs must be considered a top priority in the development of smartphone apps. [ABSTRACT FROM AUTHOR]

A number of organizations have adopted offshore software development (OSD) practices to support their software development in terms of reducing time to market, access to technical talent and lower costs. There is a lack of understandings of the factors associated with successful management of OSD projects. The objective of this study is to identify and prioritize the factors that contribute to successful management of OSD projects. First, we conducted a systematic literature review (SLR) to identify a set of factors that influence successful management of OSD projects. Second, a questionnaire survey was developed based on the findings of the SLR to collect feedback from industry practitioners. Finally, we prioritized the success factors using the analytic hierarchy process. In total, 36 success factors were identified. Findings of the study indicate that standards and procedures, change management engineering and trust building between distributed teams are key factors for the successful execution OSD projects. [ABSTRACT FROM AUTHOR]

In the context of video-based image classification, image annotation plays a vital role in improving the image classification decision based on it's semantics. Though, several methods have been introduced to adopt the image annotation such as manual and semi-supervised. However, formal specification, high cost, high probability of errors and computation time remain major issues to perform image annotation. In order to overcome these issues, we propose a new image annotation technique which consists of three tiers namely frames extraction, interest point's generation, and clustering. The aim of the proposed technique is to automate the label generation of video frames. Moreover, an evaluation model to assess the effectiveness of the proposed technique is used. The promising results of the proposed technique indicate the effectiveness (77% in terms of Adjusted Random Index) of the proposed technique in the context label generation for video frames. In the end, a comparative study analysis is made between the existing techniques and proposed methodology. [ABSTRACT FROM AUTHOR]

Several software design patterns have cataloged either with canonical or as variants to solve a recurring design problem. However, novice designers mostly adopt patterns without considering their ground reality and relevance to design problems, which causes to increase the development and maintenance efforts. The existing automated systems to select the design patterns need either high computing effort and time for the formal specification or precise learning through the training of several classifiers with large sample size to select the right design patterns realized on the base of their ground reality. In order to discuss this issue, we propose a method on the base of a supervised learning technique named 'Learning to Rank', to rank the design patterns via the text relevancy with the description of the given design problems. Subsequently, we also propose an evaluation model to assess the effectiveness of the proposed method. We evaluate the efficacy of the proposed method in the context of several design pattern collections and relevant design problems. The promising experimental results indicate the applicability of the proposed method as a recommendation system to select the right design pattern(s). [ABSTRACT FROM AUTHOR]

As an emerging and promising approach, crowdsourcing-based software development has become popular in many domains due to the participation of talented pool of developers in the contests, and to promote the ability of requesters (or customers) to choose the 'wining' solution with respect to their desired quality levels. However, due to lack of a central mechanism for team formation, continuity in the developer's work on consecutive tasks and risk of noise in submissions of a contest, there is a gap between the requesters of a domain and their quality concerns related to the adaptation of a crowdsourcing-based software development platform. In order to address concerns and aid requesters, we describe three measures; Quality of Registrant Developers (QRD), Quality of Contest (QC) and Quality of Support (QS) to compute and predict the quality of a crowdsourcing-based platform through historical information on its completed tasks. We evaluate the capacity of the QRD, QC and QS as assessors to predict the quality. Subsequently, we implement a crawler to mine the information of completed development tasks from the TopCoder platform to inspect the proposed measures. The promising results of our QRD, QC, and QS measures suggest to use the proposed measures to the requesters and researchers of other domains such as pharmaceutical research and development, in order to investigate and predict the quality of crowdsourcing-based software development platforms. [ABSTRACT FROM AUTHOR]

Requirements uncertainty and conflict are the two pervasive phenomena that are currently receiving more attention in the development of information system (IS) projects. The existing literature based on requirements engineering indicates that, as a result of frequent interactions between users and IS practitioners, requirements uncertainty can negatively affect team performance. However, the nature of conflicts and requirements uncertainty have limited exploratory power with regard to software team effectiveness. The main objective of this work is to obtain additional insight into this phenomena by examining the relationships between requirements uncertainty (which comprises both requirements instability and variability) and conflict (i.e. task and relationship conflict). Furthermore, the impact of task and relationship conflict on team effectiveness has also been investigated separately. In order to address these relationships, a theoretical research model was developed and validated through an empirical study based on an online questionnaire survey. We gathered 71 complete responses, mostly from experts within the Indian software industry. The findings of this study indicate that there is a strong positive relationship between requirements instability and variability and relationship conflict; however, task conflict task conflict was not found to have a significant relationship with these dimensions. Additionally, we have observed a strong stronger negative effect of relationship conflict on team effectiveness than that of task conflict during the requirements uncertainty. [ABSTRACT FROM AUTHOR]

The value of data to a company means that it must be protected. When it comes to safeguarding their local and worldwide databases, businesses face a number of challenges. To systematically review the literature to highlight the difficulties in establishing, implementing, and maintaining secure databases. In order to better understand database system problems, we did a systematic mapping study (SMS). We’ve analyzed 100 research publications from different digital libraries and found 20 issues after adopting inclusion and exclusion criteria. This SMS study aimed to identify the most up-to-date research in database security and the different challenges faced by users/clients using various databases from a software engineering perspective. In total, 20 challenges were identified related to database security. Our results show that “weak authorization system”, “weak access control”, “privacy issues/data leakage”, “lack of NOP security”, and “database attacks” as the most frequently cited critical challenges. Further analyses were performed to show different challenges with respect to different phases of the software development lifecycle, venue of publications, types of database attacks, and active research institutes/universities researching database security. The organizations should implement adequate mitigation strategies to address the identified database challenges. This research will also provide a direction for new research in this area. [ABSTRACT FROM AUTHOR]

Human action recognition using skeletons has become increasingly appealing to a growing number of researchers in recent years. It is particularly challenging to recognize actions when they are captured from different angles because there are so many variations in their representations. This paper proposes an automatic strategy for determining virtual observation viewpoints that are based on learning and data driven to solve the problem of view variation throughout an act. Our VA-CNN and VA-RNN networks, which use convolutional and recurrent neural networks with long short-term memory, offer an alternative to the conventional method of reorienting skeletons according to a human-defined earlier benchmark. Using the unique view adaption module, each network first identifies the best observation perspectives and then transforms the skeletons for end-to-end detection with the main classification network based on those viewpoints. The suggested view adaptive models can provide significantly more consistent virtual viewpoints using the skeletons of different perspectives. By removing views, the models allow networks to learn action-specific properties more efficiently. Furthermore, we developed a two-stream scheme (referred to as VA-fusion) that integrates the performance of two networks to obtain an improved prediction. Random rotation of skeletal sequences is used to avoid overfitting during training and improve the reliability of view adaption models. An extensive experiment demonstrates that our proposed view adaptive networks outperform existing solutions on five challenging benchmarks. [ABSTRACT FROM AUTHOR]