This study investigates factors influencing Vietnamese university students' adoption of Generative AI in learning, using an extended Unified Theory of Acceptance and Use of Technology model including attitude and personal innovativeness. Using data from 396 students analyzed via PLS-SEM, all hypotheses were supported: Performance expectancy and effort expectancy positively influence attitude; facilitating conditions enhance effort expectancy and behavioral intention; attitude predicts behavioral intention, which in turn drives adoption of AI in higher education; and personal innovativeness strongly predicts adoption. The model explains a significant portion of variance in adoption, highlighting mediating roles of attitude and behavioral intention. Findings emphasize that, beyond performance and effort expectancy, individual innovativeness and institutional support are key to responsible generative artificial intelligence integration in higher education, contributing theoretically to the model and practically to policy in developing countries.
Although OSS has gained traction across industries worldwide, few studies have been reported on OSS adoption in the financial industry and even less in the context of developing countries. This paper presents an exploratory overview of the state of OSS adoption in the financial industry using the case of South Africa as a country with the largest and most developed financial sector in Africa. To achieve this, we conducted a qualitative study that leveraged the Technology-Organisation-Environment (TOE) framework, and the Diffusion of Innovation (DOI) theory as theoretical references and used thematic analysis to analyse the data collected from a focus group discussion (FGD) of eight experts from four financial services organisations based in South Africa. The study's findings reveal the state of practice of OSS and the technological, organisational, and environmental factors that affect OSS adoption, and diffusion in financial services organisations in South Africa. We also identified seven research themes that should gain the attention of researchers from now on.
Traditional software development is shifting toward the open-source development model, particularly in the current environment of competitive challenges to develop software openly. The author employs a case study approach to investigate how organizations and their affiliated developers collaborate in the open-source software (OSS) ecosystem TensorFlow (TF). The analysis of the artificial intelligence OSS library TF combines social network analysis (SNA) and an examination of archival data by mining software repositories. The study looks at the structure and evolution of code-collaboration among developers and with the ecosystem's organizational networks over the TF lifespan. These involved organizations play a particularly critical role in development. The research also looks at productivity, homophily, development, and diversity among developers. The results deepen the understanding of OSS communities' collaborative developer and organization patterns. Furthermore, the study emphasizes the importance and evolution of social networks, diversity, and productivity in ecosystems.
Classification of software defects is an important task to know the type of defects. It helps to prioritize the defects, to understand the cause of defects for improving the process of software defect management system by taking the appropriate action. In this paper, we evaluate the performance of naïve Bayes, support vector machine, k nearest neighbor, random forest, and decision tree machine learning algorithm to classify the software defect based on orthogonal defect classification by selecting the relevant features using chi-square score. Standard metrics accuracy, precision, and recall are calculated separately for Cassandra, HBase, and MongoDB datasets. The proposed method improves the existing approach in terms of accuracy by 5%, 20%, 6%, 27%, and 26% for activity, defect impact, target, type, and qualifier respectively, and shows the enhanced performance.
Mining Software Repositories in collaborative environment during software evolution or maintenance faces challenges due to creation of larger than necessary slices or unnecessary splitting of Revision History and detection of edge level changes. Due to these limitations, GIT and Diff & Merge Tools does not accurately detect the similarities and changes between versions due to renaming or shifting. Detection of these similarities accurately helps to detect code clones and change patterns that improves understandability, knowledge transfer and tracking changes. Therefore, the authors proposed fuzzy based hybrid technique to detect the similarities/changes between versions considering RS by enriching the Revision History at three granularities- File, Class and Method level. 30% more entities have been found similar/change by deriving Classification model with f-score and ROC Area more than 0.985 and .994 respectively for all applications. Hence, proposed technique improves productivity, reusability and maintainability with respect to VCA.
