Addressing Communication, Coordination and Cultural Issues in Global Software Development Projects

The field of Global Software Development has been an active area of research for the last two decades due to its enormous benefits such as lower labor cost, faster development and easy access to the skilled labor pool. Apart from these benefits, it faces some challenges like communication, coordination, trust and configuration management etc. These challenges arise primarily due to physical, cultural and time zone differences. The empirical studies highlight that the existing Global Software Development solutions do not fully meet the user needs as there are still several gaps in these solutions. Therefore, to fulfill these gaps, there is a need to develop novel frameworks that address outstanding issues. In this paper, we have attempted to address the aforesaid GSD challenges. The practitioners can benefit from our proposed framework during the execution of GSD projects. The proposed framework mainly focuses on the root causes of the two principal challenges namely the communication and cultural differences. We believe that if the team members of a software project can communicate effectively and show considerations for others by imparting due reverence to the cultural norms, then the other residual issues can easily be reduced and minimized

Automating Test Case Generation for Android Applications using Model-based Testing

Testing of mobile applications (apps) has its quirks as numerous events are required to be tested. Mobile apps testing, being an evolving domain, carries certain challenges that should be accounted for in the overall testing process. Since smartphone apps are moderate in size so we consider that model-based testing (MBT) using state machines and statecharts could be a promising option for ensuring maximum coverage and completeness of test cases. Using model-based testing approach, we can automate the tedious phase of test case generation, which not only saves time of the overall testing process but also minimizes defects and ensures maximum test case coverage and completeness. In this paper, we explore and model the most critical modules of the mobile app for generating test cases to ascertain the efficiency and impact of using model-based testing. Test cases for the targeted model of the application under test were generated on a real device. The experimental results indicate that our framework reduced the time required to execute all the generated test cases by 50%. Experimental setup and results are reported herein

Ethernet Network Functionality Testing

Ethernet functionality testing as a generic term used for checking connectivity,throughput and capability to transfer packets over the network. Especially in the packet-switchenvironment, Ethernet testing has become an essential part for deploying a reliable network.Over a long distance Ethernet testing parameter for analyzing network performance must havetwo devices attached and synchronized. Saab Microwave Systems is among the leading suppliers of radar systems developing groundbased,naval and air-borne radar systems. To ensure the correct functionality, the developerwants to verify the performance of computer network and looking for a suitable solution. A software application is required to verify and test the functionality of the Ethernet network andto verify the functionality and performance of the TCP/IP stack of newly added node. Theprograms shall be easily ported to different operating systems and must not depend on specificproduct properties.A software application, “NetBurst”, is developed for Ethernet functionality testing. Theapplication is vendor and platform independent.

Predicting the Environmental Change of Carbon Emission Patterns in South Asia: A Deep Learning Approach Using BiLSTM

China’s economy has made significant strides in the past three decades. As a direct result of China’s “one belt, one road” (OBOR) initiative, the country’s rate of industrialization and urbanization is currently the fastest in the entire world. This rapid development is largely dependent on the enormous amounts of energy currently being consumed and forms the foundation of the world’s high levels of carbon emissions. It is generally agreed that the production of greenhouse gases, particularly carbon dioxide, is the primary contributor to the current state of climate change. In this paper, a CO2 emission prediction model based on Bi-LSTM is constructed. In order to conduct empirical tests on the model, this study uses data from South Asian countries and China from 2001 to 2020. China’s CO2 emissions from 2022 to 2030 were predicted along with those of other countries in order to study the combined effects of the scientific and technological progress, industrial structures, and energy structure factors affecting CO2 emissions. When compared with the LSTM and GRU methods, the Bi-LSTM model’s results produced lower MAE, MSE, and MAPE values, indicating that it performs better. According to the findings, carbon emissions represent a significant problem that will become much worse in the future due to China and India’s high emissions, particularly in the next 10 years, if the government does not implement policies that help reduce those emissions

Multimodal integration for data-driven classification of mental fatigue during construction equipment operations: Incorporating electroencephalography, electrodermal activity, and video signals

Construction equipment operations that require high levels of attention can cause mental fatigue, which can lead to inefficiencies and accidents. Previous studies classified mental fatigue using single-modal data with acceptable accuracy. However, mental fatigue is a multimodal problem, and no single modality is superior. Moreover, none of the previous studies in construction industry have investigated multimodal data fusion for classifying mental fatigue and whether such an approach would improve mental fatigue detection. This study proposes a novel approach using three machine learning models and multimodal data fusion to classify mental fatigue states. Electroencephalography, electrodermal activity, and video signals were acquired during an excavation operation, and the decision tree model using multimodal sensor data fusion outperformed other models with 96.2% accuracy and 96.175%–98.231% F1 scores. Multimodal sensor data fusion can aid in the development of a real-time system to classify mental fatigue and improve safety management at construction sites