How personality traits are interrelated with team climate and team performance in software engineering?: a preliminary study

Personality and its impacts on team processes in the domain of software engineering have been an area of investigation for many researchers since the last many years. However, personality traits and its effects on team climate and team performance were not being focused as area of research. In our previous research, we had performed a systematic literature review on team climate and team productivity. In progression of our earlier work in this paper, we have extended the work and take personality traits as an independent variable over team climate and performance. This paper reports the results of preliminary data survey, which has been conducted to measure the effects of personality on team climate and team performance. Results show the strong and positive correlation among personality factor Extraversion, team climate and team performance variables

Factors influence novice programmers toward test first approach

Test First is one of the Agile development approaches. In practice, Test First needs a developer to design test cases followed by the development of actual codes. The previous study on Test First has been covering the quality of the codes, either internal quality of codes, external quality of codes, or productivity of codes. Also, research on the behavior of the developers toward Test First based on the developers experiences implementing the Test First approach. This research is looking into the behavior of developers, which focus on finding the factors that influence novice programmers’ to execute Test First by using the Theory of Planned Behavior as the theoretical framework. The Theory of Planned Behavior framework is used to identify the factors that contribute to the Intention of novice programmers’ to implement Test First. The factors were identified quantitatively using a set of questionnaire. The results indicated that Behavioral Beliefs, Attitude towards Behavior, Normative Beliefs, and Subjective Norms are the factors that influenced novice programmers to implement Test First. © 2019, World Academy of Research in Science and Engineering. All rights reserved

Least square-support vector machine based brain tumor classification system with multi model texture features

Radiologists confront formidable challenges when confronted with the intricate task of classifying brain tumors through the analysis of MRI images. Our forthcoming manuscript introduces an innovative and highly effective methodology that capitalizes on the capabilities of Least Squares Support Vector Machines (LS-SVM) in tandem with the rich insights drawn from Multi-Scale Morphological Texture Features (MMTF) extracted from T1-weighted MR images. Our methodology underwent meticulous evaluation on a substantial dataset encompassing 139 cases, consisting of 119 cases of aberrant tumors and 20 cases of normal brain images. The outcomes we achieved are nothing short of extraordinary. Our LS-SVM-based approach vastly outperforms competing classifiers, demonstrating its dominance with an exceptional accuracy rate of 98.97%. This represents a substantial 3.97% improvement over alternative methods, accompanied by a notable 2.48% enhancement in Sensitivity and a substantial 10% increase in Specificity. These results conclusively surpass the performance of traditional classifiers such as Support Vector Machines (SVM), Radial Basis Function (RBF), and Artificial Neural Networks (ANN) in terms of classification accuracy. The outstanding performance of our model in the realm of brain tumor diagnosis signifies a substantial leap forward in the field, holding the promise of delivering more precise and dependable tools for radiologists and healthcare professionals in their pivotal role of identifying and classifying brain tumors using MRI imaging techniques

Preliminary study on factors affecting E-commerce success: a modified Delone and McLean model

Electronic commerce has gained the popularity since last two decades, due to its recompenses towards the online transactions. Academicians and e-retailers eagerly want to identify the factors which affect the e-commerce success. The main intention of this study to investigate the factors which are essential to draw the net benefits of e-commerce from an individual perspective rather than the organization perspective. The proposed framework in this paper is based on Mclean and Delone 2003 IS success model along with two extra variables that are privacy and trust. In this paper, the author discussed the result of the pilot study. The authors designed the quantitative research questionnaire by adopting the items from the previous studies and conducted the pilot study to check the reliability of the questionnaire. The overall value for Cronbach's alpha was 0.89 and also the Cronbach's alpha value for individual constructs were greater than 0.7, which indicates that there is a strong relation between the items. This research engrossed on four factors that are system quality, service quality, privacy, trust, that are essential for achieving the user satisfaction which in turn leads to achieve the e-commerce net benefits. In addition to service quality and system quality, trust and privacy are the important factors that affect the net benefits through user satisfaction, hence the modified version of Delone and Mclean IS success model from individual perspective is proposed

The effect of software engineers\u27 personality traits on team climate and performance: a systematic literature review

Context Over the past 50 years numerous studies have investigated the possible effect that software engineers\u27 personalities may have upon their individual tasks and teamwork. These have led to an improved understanding of that relationship; however, the analysis of personality traits and their impact on the software development process is still an area under investigation and debate. Further, other than personality traits, "team climate" is also another factor that has also been investigated given its relationship with software teams\u27 performance. Objective The aim of this paper is to investigate how software professionals\u27 personality is associated with team climate and team performance. Method In this paper we detail a Systematic Literature Review (SLR) of the effect of software engineers\u27 personality traits and team climate on software team performance. Results Our main findings include 35 primary studies that have addressed the relationship between personality and team performance without considering team climate. The findings showed that team climate comprises a wide range of factors that fall within the fields of management and behavioral sciences. Most of the studies used undergraduate students as subjects and as surrogates of software professionals. Conclusions The findings from this SLR would be beneficial for understanding the personality assessment of software development team members by revealing the traits of personality taxonomy, along with the measurement of the software development team working environment. These measurements would be useful in examining the success and failure possibilities of software projects in development processes. General terms Human factors, performance

A systematic review of the effects of team climate on software team productivity

The term team-work has been a significant topic in software engineering over the past 50 years. The team climate is the exchange of ideas and perceptions among team members in favor to promote the innovation in work processes. In this paper, we presented our work on a systematic review on the effect of team climate on the software productivity or performance. The summarized results from this research would be useful for achieving effectiveness in software engineering work teams

A Deep Learning-Based Model for Date Fruit Classification

A total of 8.46 million tons of date fruit are produced annually around the world. The date fruit is considered a high-valued confectionery and fruit crop. The hot arid zones of Southwest Asia, North Africa, and the Middle East are the major producers of date fruit. The production of dates in 1961 was 1.8 million tons, which increased to 2.8 million tons in 1985. In 2001, the production of dates was recorded at 5.4 million tons, whereas recently it has reached 8.46 million tons. A common problem found in the industry is the absence of an autonomous system for the classification of date fruit, resulting in reliance on only the manual expertise, often involving hard work, expense, and bias. Recently, Machine Learning (ML) techniques have been employed in such areas of agriculture and fruit farming and have brought great convenience to human life. An automated system based on ML can carry out the fruit classification and sorting tasks that were previously handled by human experts. In various fields, CNNs (convolutional neural networks) have achieved impressive results in image classification. Considering the success of CNNs and transfer learning in other image classification problems, this research also employs a similar approach and proposes an efficient date classification model. In this research, a dataset of eight different classes of date fruit has been created to train the proposed model. Different preprocessing techniques have been applied in the proposed model, such as image augmentation, decayed learning rate, model checkpointing, and hybrid weight adjustment to increase the accuracy rate. The results show that the proposed model based on MobileNetV2 architecture has achieved 99% accuracy. The proposed model has also been compared with other existing models such as AlexNet, VGG16, InceptionV3, ResNet, and MobileNetV2. The results prove that the proposed model performs better than all other models in terms of accuracy

A Convolution Neural Network-Based Seed Classification System

International audienceOver the last few years, the research into agriculture has gained momentum, showing signs of rapid growth. The latest to appear on the scene is bringing convenience in how agriculture can be done by employing various computational technologies. There are lots of factors that affect agricultural production, with seed quality topping the list. Seed classification can provide additional knowledge about quality production, seed quality control and impurity identification. The process of categorising seeds has been traditionally done based on characteristics like colour, shape and texture. Generally, this is performed by specialists by visually inspecting each sample, which is a very tedious and time-consuming task. This procedure can be easily automated, providing a significantly more efficient method for seed sorting than having them be inspected using human labour. In related areas, computer vision technology based on machine learning (ML), symmetry and, more particularly, convolutional neural networks (CNNs) have been generously applied, often resulting in increased work efficiency. Considering the success of the computational intelligence methods in other image classification problems, this research proposes a classification system for seeds by employing CNN and transfer learning. The proposed system contains a model that classifies 14 commonly known seeds with the implication of advanced deep learning techniques. The techniques applied in this research include decayed learning rate, model checkpointing and hybrid weight adjustment. This research applies symmetry when sampling the images of the seeds during data formation. The application of symmetry generates homogeneity with regards to resizing and labelling the images to extract their features. This resulted in 99% classification accuracy during the training set. The proposed model produced results with an accuracy of 99% for the test set, which contained 234 images. These results were much higher than the results reported in related research

Smart Seed Classification System based on MobileNetV2 Architecture

The agricultural transformation in the last decade using artificial intelligence has led to significant gains in productivity and profitability. The traditional machine learning approaches present inherent limitations in extracting features and information from image data. Deep learning techniques, particularly CNN’s, help to overcome these limitations due to their multi-level architecture. Various deep learning applications in agriculture include crop disease identification, fruit classification, and germination rate monitoring. Seed image analysis is considered a significant task for the preservation of biodiversity and sustainability. This research uses MobileNetV2, a deep learning convolutional neural network (DCNNs) for seed classification. This model has been preferred due to its simple architecture and memory-efficient characteristics. A total of 14 different classes of seeds were used for the experimentation. The results indicate accuracies of 98% and 95% on training and test sets, respectively