Perceptions of ICT Practitioners Regarding Software Privacy

Examining the influence of technological self-efficacy, perceived trust, security, and electronic word of mouth on ICT usage in the education sector

The context of education has changed due to revolutionary developments in the information communication technology (ICT) industry in the post-COVID era. Innovative learning methods were introduced in the education sector to promote quality education. The students find it more convenient to use ICT tools to integrate their knowledge-seeking. China has recently paid more attention to developing and adopting electronic infrastructure. The study assesses the effect of technology self-efficacy (TSE) on ICT acceptance and implementation in China's education sector. It also analyzed the role of perceived trust, perceived security, and electronic word of mouth (eWOM) in integrating digital information sharing and interaction tools. Data is collected from 382 business students at Chinese universities. The results revealed that perceived trust mediates the relationship between TSE and the actual use of ICT tools, intention to use ICT tools for information, and intention to use ICT tools for interaction. Further, perceived security and eWOM significantly moderate the relationship between TSE and perceived trust. The findings indicate that it is essential to offer assistance and instruction to students in the educational sector so they can use ICT technology more frequently. It is also crucial for organizations to establish a supportive culture and provide the necessary technological resources to facilitate the use of ICT.

Guidelines adopted by agile teams in privacy requirements elicitation after the Brazilian general data protection law (LGPD) implementation

The Brazilian General Data Protection Law (LGPD) implementation has impacted activities carried out by the software development teams. Due to it, developers had to become aware of the existing techniques and tools to carry out privacy requirements elicitation. Extending our previous work, we have investigated the actions taken by organizations regarding the LGPD, specifically in software development, considering the perception of agile development teams after two years of the LGPD implementation. In addition, we also investigated the perception of an agile team regarding the practices, techniques, and tools previously cited by practitioners as potential solutions for use in this context, along with techniques already in use in the current context. We have conducted a systematic literature review (SLR) and selected 36 primary studies. Furthermore, we have conducted a survey with 53 IT practitioners and semi-structured interviews with ten practitioners. The LGPD principles are known by most agile teams and are being implemented by the organizations, although the existing tools to support privacy requirements elicitation are still underused by agile teams. Moreover, agile teams consider that software requirements and software construction are the most impacted areas of knowledge by the LGPD, and most of them use user stories in privacy requirements elicitation. Our findings reveal that agile teams and Brazilian organizations are more concerned with user data privacy issues after the LGPD became effective. However, agile teams still face challenges in privacy requirements elicitation.

Diagnostic of Data Processing by Brazilian Organizations—A Low Compliance Issue

In order to guarantee the privacy of users’ data, the Brazilian government created the Brazilian General Data Protection Law (LGPD). This article made a diagnostic of Brazilian organizations in relation to their suitability for LGPD, based on the perception of Information Technology (IT) practitioners who work in these organizations. We used a survey with 41 questions to diagnose different Brazilian organizations, both public and private. The diagnostic questionnaire was answered by 105 IT practitioners. The results show that 27% of organizations process personal data of public access based on good faith and LGPD principles. In addition, our findings also revealed that 16.3% of organizations have not established a procedure or methodology to verify that the LGPD principles are being respected during the development of services that will handle personal data from the product or service design phase to its execution and 20% of the organizations did not establish a communication process to the personal data holders, regarding the possible data breaches. The result of the diagnostic allows organizations and data users to have an overview of how the treatment of personal data of their customers is being treated and which points of attention are in relation to the principles of LGPD.

Software Requirements Classification Using Machine Learning Algorithms

The correct classification of requirements has become an essential task within software engineering. This study shows a comparison among the text feature extraction techniques, and machine learning algorithms to the problem of requirements engineer classification to answer the two major questions "Which works best (Bag of Words (BoW) vs. Term Frequency-Inverse Document Frequency (TF-IDF) vs. Chi Squared (CHI2)) for classifying Software Requirements into Functional Requirements (FR) and Non-Functional Requirements (NF), and the sub-classes of Non-Functional Requirements?" and "Which Machine Learning Algorithm provides the best performance for the requirements classification task?". The data used to perform the research was the PROMISE_exp, a recently made dataset that expands the already known PROMISE repository, a repository that contains labeled software requirements. All the documents from the database were cleaned with a set of normalization steps and the two feature extractions, and feature selection techniques used were BoW, TF-IDF and CHI2 respectively. The algorithms used for classification were Logist Regression (LR), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB) and k-Nearest Neighbors (kNN). The novelty of our work is the data used to perform the experiment, the details of the steps used to reproduce the classification, and the comparison between BoW, TF-IDF and CHI2 for this repository not having been covered by other studies. This work will serve as a reference for the software engineering community and will help other researchers to understand the requirement classification process. We noticed that the use of TF-IDF followed by the use of LR had a better classification result to differentiate requirements, with an F-measure of 0.91 in binary classification (tying with SVM in that case), 0.74 in NF classification and 0.78 in general classification. As future work we intend to compare more algorithms and new forms to improve the precision of our models.