Software organization always aims at developing a quality software product using the estimated development resources, effort, and time. Global Software Development (GSD) has emerged as an essential tool to ensure optimal utilization of resources, which is performed in globally distributed settings in various geographical locations. Global software engineering focuses on reducing the cost, increasing the development speed, and accessing skilled developers worldwide. Estimating the required amount of resources and effort in the distributed development environment remains a challenging task. Thus, there is a need to focus on cost estimation models in the GSD context. We nevertheless acknowledge that several cost estimation techniques have been reported. However, to the best of our knowledge, the existing cost estimation techniques/models lack considering the additional cost drivers required to compute the accurate cost estimation in the GSD context. Motivated by this, the current work aims at identifying the other cost drivers that affect the cost estimation in the context of GSD. To achieve the targeted objectives, current state-of-the-art related to existing cost estimation techniques of GSD is reported. We adopted SLR and Empirical approach to address the formulated research questions. The current study also identifies the missing factors that would help the practitioners improve the cost estimation models. The results indicate that previously conducted work ignores the additional elements necessary for the cost estimation in the GSD context. Moreover, the current work proposes a conceptual cost estimation model tailored to fit the GSD context.

The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent threats (APT) and especially botnets are the foremost promising and potential attacks that may throw the complete industrial IoT network into chaos. IIoT-enabled botnets are highly scalable, technologically diverse, and highly resilient to classical and conventional detection mechanisms. Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. The proposed approach is thoroughly evaluated on a current state-of-the-art, publicly available dataset using standard performance evaluation metrics. Moreover, our proposed technique has been precisely verified with our constructed hybrid DL-enabled architectures and current benchmark DL algorithms. Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in prevalent IIoTs. Besides, we have cross-validated our results to show utterly unbiased performance.

Software development outsourcing is becoming more and more famous because of the advantages like cost abatement, process enhancement, and coping with the scarcity of needed resources. Studies confirm that unfortunately a large proportion of the software development outsourcing projects fails to realize anticipated benefits. Investigations into the failures of such projects divulge that in several cases software development outsourcing projects are failed because of the issues that are associated with requirements engineering process. The objective of this study is the identification and the ranking of the commonly occurring issues of the requirements engineering process in the case of software development outsourcing. For this purpose, contemporary literature has been assessed rigorously, issues faced by practitioners have been identified and three questionnaire surveys have been organized by involving experienced software development outsourcing practitioners. The Delphi technique, cut-off value method and 50% rule have also been employed. The study explores 150 issues (129 issues from literature and 21 from industry) of requirements engineering process for software development outsourcing, groups the 150 issues into 7 identified categories and then extricates 43 customarily or commonly arising issues from the 150 issues. Founded on ‘frequency of occurrence’ the 43 customarily arising issues have been ranked with respect to respective categories (category-wise ranking) and with respect to all the categories (overall ranking). Categories of the customarily arising issues have also been ranked. The issues’ identification and ranking contribute to design proactive software project management plan for dealing with software development outsourcing failures and attaining conjectured benefits of the software development outsourcing.

Global Software Development (GSD) is widely used by software development organizations to ensure the development of a cost-effective software product. GSD has now become a common engineering practice adopted by a significant number of multinational software development organizations, and even individuals (freelancers) are seeking numerous benefits including low development cost, highly skilled workers, and access to better development ideas. However, communication and coordination challenges remain a prominent research issue in the GSD context, while performing different project-related activities especially for Requirements Change Management (RCM). As a result, improper communication and coordination during RCM require additional time, cost, and development resources. Thus, it is of vital importance to ensure proper communication and coordination before initiating a software project. Inspired by this, current work aims at exploring and mitigating the communication and coordination challenges during RCM in the GSD context. To accomplish the targeted research objective, we performed a tertiary study to provide a landscape of the challenges that occurred during RCM in the context of GSD. Based on the performed study, we found 62 communication and 14 coordination challenges. In total, 107 mitigation strategies are explored and reported that effectively address the categorized sub-challenges of communication and coordination. Moreover, we proposed a conceptual model useful to address the communication and coordination challenges for the RCM process in GSD. Furthermore, we consulted the domain experts for the validation of the proposed conceptual model. Based on the promising results, we believe that this work supports the project managers in managing the cost and time-related issues in the GSD context. Consequently, the proposed conceptual model would help in optimally utilizing the scared software development resources.

Technology plays an important role in our society, especially in the field of education. It is quite regrettable that people, particularly the deaf, still face a lot of challenges in acquiring an education. Their learning methods are different as compared to hearing people. They use Sign Language (SL) rather than natural language to communicate and learn. They are required to put a lot of effort into learning different concepts using conventional pedagogies. Therefore, there is a dire need for some assistive technology to improve their learn-ability and understandability. In the present study, 192 deaf participants aged 5–10 years were sampled from two special child institutes. The objective of the present study was to determine the effectiveness of a mobile interface through a developed mobile application for learning basic mathematical concepts using Pakistan Sign Language (PSL). The present study bridges the gap between the technology-based method and conventional teaching methods, which are used for teaching mathematical concepts using PSL. The participants were divided into two groups, that is, one group learned through conventional methods (flash cards and board) and the other group through the developed mobile application. The difference in the performance of both the groups was evaluated by conducting quizzes. The quiz results were analyzed by the Z-test and ANOVA. The findings revealed that the Experimental Group (EG) participants, who were instructed by our mobile application showed higher proficiency in the quizzes as compared to the Control Group (CG). EG participants performed better than CG by 12% in the quizzes. A gender based difference was also observed for the quiz scores. Male participants in category C (word problem learning) performed 2.7% better than females in the EG and 2.5% better in the CG. Moreover, a significant difference was also observed in the time taken by participants in both groups to complete the quizzes. CG participants took 20 min longer than EG participants to complete the quizzes. The results of the ANOVA showed that the quiz scores were directly affected by the mode of teaching used for participants in both groups.

Activity landscapes (ALs) are graphical representations that combine compound similarity and activity data. ALs are constructed for visualizing local and global structure–activity relationships (SARs) contained in compound data sets. Three-dimensional (3D) ALs are reminiscent of geographical maps where differences in landscape topology mirror different SAR characteristics. 3D AL models can be stored as differently formatted images and are thus amenable to image analysis approaches, which have thus far not been considered in the context of graphical SAR analysis. In this proof-of-concept study, 3D ALs were constructed for a variety of compound activity classes and 3D AL image variants of varying topology and information content were generated and classified. To these ends, convolutional neural networks (CNNs) were initially applied to images of original 3D AL models with color-coding reflecting compound potency information that were taken from different viewpoints. Images of 3D AL models were transformed into variants from which one-dimensional features were extracted. Other machine learning approaches including support vector machine (SVM) and random forest (RF) algorithms were applied to derive models on the basis of such features. In addition, SVM and RF models were trained using other features obtained from images through edge filtering. Machine learning was able to accurately distinguish between 3D AL image variants with different topology and information content. Overall, CNNs which directly learned feature representations from 3D AL images achieved highest classification accuracy. Predictive performance for CNN, SVM, and RF models was highest for image variants emphasizing topological elevation. In addition, SVM models trained on rudimentary images from edge filtering classified such images with high accuracy, which further supported the critical role of altitude-dependent topological features for image analysis and predictions. Taken together, the findings of our proof-of-concept investigation indicate that image analysis has considerable potential for graphical SAR exploration to systematically infer different SAR characteristics from topological features of 3D ALs.