Human aspect is considered as one of the success factors for software development. Besides, diversity is labeled as the most effective element for creating effectiveness in team performance. Thus, this study has aimed to find the effects of gender diversity on team performance based on personality types. The personality types' variations were found effective on team performance based on genders. For instance, male-lead groups were found convenient with both genders but female-lead groups looked much comfortable with only female members. Moreover, female-gender looked uneasy in male dominant teams especially when their personality is E (extrovert). Whereas, male-gender was observed as digestible in female-lead teams if the I (introvert) personality is dominating.

Refactoring is one of the most widely used techniques in practice to improve the quality of existing software. However, it is observed that refactoring does not continually improve all software quality attributes. Recent studies indicated that different refactoring techniques have significantly different, sometimes opposite, and conflicting effects on software quality attributes. In other words, there is contradictory evidence on the refactoring benefit. As a result, developers face challenges in selecting appropriate refactoring techniques when they use them to improve software quality. To the best of our knowledge, no study has investigated factors that may explain inconsistent or diverging results concerning the effect of refactoring techniques on software quality. Therefore, in this study, scenarios of using refactoring techniques factor have been identified, investigated, and thoroughly analyzed. Ten of the most commonly used refactoring techniques in practice have been chosen and individually applied in seven case studies of varying sizes (small, medium, and large). The Quality Model for Object-Oriented Design (QMOOD) is used to assess how refactoring techniques affect quality attributes. The findings provide strong evidence that this factor plays a significant role in producing the various effects of refactoring techniques on quality attributes. These findings can help software developers understand how to use refactoring techniques to improve software quality while taking this factor into account. The best scenario for using each refactoring technique to improve software system quality has been identified. The findings can provide guidelines for software developers to use refactoring techniques to improve the quality of software systems based on the best scenarios of using the refactoring techniques.

The expenses associated with software maintenance and evolution constitute a significant portion, surpassing more than 80% of the overall costs involved in software development. Refactoring, a widely embraced technique, plays a crucial role in streamlining and minimizing maintenance activities and expenses. However, the effect of refactoring techniques on quality attributes presents inconsistent and conflicting findings, making it challenging for software developers to enhance software quality effectively. Additionally, the absence of a comprehensive framework further complicates the decision-making process for developers in selecting appropriate refactoring techniques aligned with specific design objectives. In light of these considerations, this research aims to introduce a novel framework for classifying refactoring techniques based on their measurable influence on internal quality attributes. Initially, an exploratory study was conducted to identify commonly employed refactoring techniques, followed by an experimental analysis involving five case studies to evaluate the effects of these techniques on internal quality attributes. Subsequently, the framework was constructed based on the outcomes of the exploratory and experimental studies, further reinforced by a multi-case analysis. Comprising three key components, namely the methodology for applying refactoring techniques, the Quality Model for Object-Oriented Design (QMOOD), and the classification scheme for refactoring techniques, this proposed framework serves as a valuable guideline for developers. By comprehending the effect of each refactoring technique on internal quality attributes, developers can make informed decisions and select suitable techniques to enhance specific aspects of their software. Consequently, this framework optimizes developers’ time and effort by minimizing the need to weigh the pros and cons of different refactoring techniques, potentially leading to a reduction in maintenance activities and associated costs.

In IoT environment applications generate continuous non-stationary data streams with in-built problems of concept drift and class imbalance which cause classifier performance degradation. The imbalanced data affects the classifier during concept detection and concept adaptation. In general, for concept detection, a separate mechanism is added in parallel with the classifier to detect the concept drift called a drift detector. For concept adaptation, the classifier updates itself or trains a new classifier to replace the older one. In case, the data stream faces a class imbalance issue, the classifier may not properly adapt to the latest concept. In this survey, we study how the existing work addresses the issues of class imbalance and concept drift while learning from nonstationarydata streams. We further highlight the limitation of existing work and challenges caused by other factors of class imbalance alongwith concept drift in data stream classification. Results of our survey found that, out of 1110 studies, by using our inclusion and exclusion criteria, we were able to narrow the pool of articles down to 35 that directly addressed our study objectives. The study found that issues such as multiple concept drift types, dynamic class imbalance ratio, and multi-class imbalance in presence of concept drift are still open for further research. We also observed that, while major research efforts have been dedicated to resolving concept drift and class imbalance, not much attention has been given to with-in-class imbalance, rear examples, and borderline instances when they exist with concept drift in multi-class data. This paper concludes with some suggested future directions.

This paper presents an approach in analyzing personality types, temperament and team diversity to determine software engineering (SE) teams performance. The benefits of understanding personality types and its relationships amongst team members are crucial for project success. Rough set analysis was used to analyze Myers-Briggs Type Indicator (MBTI) personality types, Keirsey temperament, team diversity, and team performance. The result shows positive relationships between these attributes.