Context. Software product planning involves product lifecycle management, roadmapping, release planning and requirements engineering. Requirements are collected and used together with criteria to define short-term plans, release plans and long-term plans, roadmaps. The different stages of the product lifecycle determine whether a product is mainly evolved, extended, or simply maintained. When eliciting requirements and identifying criteria for software product planning, the product manager is confronted with statements about customer interests that do not correspond to their needs. Analytics summarize, filter, and transform measurements to obtain insights about what happened, how it happened, and why it happened. Analytics have been used for improving usability of software solutions, monitoring reliability of networks and for performance engineering. However, the concept of using analytics to determine the evolution of a software solution is unexplored. In a context where a misunderstanding of users’ need can easily lead the effective product design to failure, the support of analytics for software product planning can contribute to fostering the realization of which features of the product are useful for the users or customers. Objective. In observation of a lack of primary studies, the first step is to apply analytics of software product planning concept in the evolution of software solutions by having an understanding of the product usage measurement. For this reason, this research aims to understand relevant analytics of users’ interaction with SaaS applications. In addition, to identify an effective way to collect right analytics and measure feature usage with respect to page-based analytics and feature-based analytics to provide decision-support for software product planning. Methods. This research combines a literature review of the state-of-the-art to understand the research gap, related works and to find out relevant analytics for software product planning. A market research is conducted to compare the features of different analytics tools to identify an effective way to collect relevant analytics. Hence, a prototype analytics tool is developed to explore the way of measuring feature usage of a SaaS website to provide decision-support for software product planning. Finally, a software simulation is performed to understand the impact of page clutter, erroneous page presentation and feature spread with respect to page-based analytics and feature-based analytics. Results. The literature review reveals the studies which describe the related work on relevant categories of software analytics that are important for measuring software usage. A software-supported approach, developed from the feature comparison results of different analytics tools, ensures an effective way of collecting analytics for product planners. Moreover, the study results can be used to understand the impact of page clutter, erroneous page representation and feature spread with respect to page-based analytics and feature-based analytics. The study reveals that the page clutter, erroneous page presentation and feature spread exaggerate feature usage measurement with the page-based analytics, but not with the feature-based analytics. Conclusions. The research provided a wide set of evidence fostering the understanding of relevant analytics for software product planning. The results revealed the way of measuring the feature usage to SaaS product managers. Furthermore, feature usage measurement of SaaS websites can be recognized, which helps product managers to understand the impact of page clutter, erroneous page presentation and feature spread between page-based and feature-based analytics. Further case study can be performed to evaluate the solution proposals by tailoring the company needs. / +46739480254

Context and Motivation: Technology play a vital role in the people’s present life. These technology has different types of softwares and devices. Now a days people mostly live around the digital devices and involve with them. These softwares when using causes digital distraction. Mostly digital distraction occurs only when using digital devices. For the development of software and devices requirements play a major role in the development in an organization. Question/Problem: The requirements are developed into the features of software. These features when using can cause distraction. So to manage these digital distraction causing by the software, we considered requirements engineering, by introducing the requirements engineering process at the beginning of the development of software, so to develop a distraction-free software. This can be helpful for the developers to develop the distraction-free software from early stages for the user. Principal Ideas/Objectives: Our thesis mainly focus on the identification of quality factors requirements contributing to the digital distraction and analyse them and we also tried to identify feature usage and user experience of software to identify distraction of software and a mind-map is designed for the study of digital distraction. So that these can provide useful information for future studies. Methods/Contributions: In our thesis study, we conducted Systematic Literature Review using snowballing process for the identification of the literature about the digital distraction and analysed the SLR. Further an online survey is conducted on Instagram users to extract distraction features and reasons for distraction and then we used this data to plan a mind-map of different categories contributing to digital distraction. Results: The main findings and observations in our research are observed through SLR and survey results. For research question 1, the data that is extracted through SLR gives quality factor requirements that contribute to the digital distraction. An understanding of digital distraction among software and feature and also the causes are observed. For the research question 2, the data from the survey is collected from the users of Instagram are observed. The results from the survey are extracted to know about the distraction of software using the feature usage which is extracted from the survey and also the user experience. From these results of SLR and survey data, a mind map is designed to know about the study of the digital distraction. Conclusions: Finally, we come up with a idea by planning mind-map that helps the software developers and requirement engineers to build a distraction-free software. The results of this study can be helpful to all the software developers and also to the ones who want to carry our the research on requirements connection with digital distraction, this can be a start point for them.

In higher education institutions, virtual learning systems (VLSs) have been adopted, and are becoming increasingly popular among educators. However, despite this ubiquity of VLS use, there has not been widespread change in pedagogic practice to take advantage of the functionality afforded by VLSs. Knowledge of the actual usage of e-learning systems is limited in terms of what specific feature sets are deemed useful, and how this influences system usage. VLSs have a suite of tools with associated functions/features and properties, as well as non-functional system characteristics. In addition, these systems incorporate pedagogic features to cater for online teaching. Educators in higher education, who are the chief agents of e-learning, are confounded by system-related, pedagogic, organisational, user difference and demographic factors that influence VLS usage. Virtual learning system usage involves system feature usage extent and frequency, total system usage and usage clusters. The aim of this study is to develop a model representing the factors that influence usage of VLSs in higher education. The links between system usage and system-related factors, pedagogic factors, organisational factors, user-difference and demographic factors is researched. This research incorporated a literature study, a pilot study, interviews and surveys. A case study research strategy was combined with a mixed methods research design. The results of the qualitative analysis was triangulated with the findings of the quantitative analysis and compared to the findings of the literature study. The study was conducted at two residential higher education institutions (HEI), namely, University of KwaZulu-Natal and Durban University of Technology. The main contribution of this study is the Virtual Learning System Usage Model (VLSUM) representing the factors that influence VLS usage in residential higher education institutions. The proposed VLSUM is based on the empirical results of this study. VLSUM can be used by managers of educational technology departments and instructional designers to implement interventions to optimize usage. The constructs of VLSUM confirmed existing theories, replicated and synthesised theories from different fields, and extended existing models to produce a new model for understanding the factors that influence VLS usage in higher education. / Computing / D. LITT. et. Phil. (Information Systems)

In higher education institutions, virtual learning systems (VLSs) have been adopted, and are becoming increasingly popular among educators. However, despite this ubiquity of VLS use, there has not been widespread change in pedagogic practice to take advantage of the functionality afforded by VLSs. Knowledge of the actual usage of e-learning systems is limited in terms of what specific feature sets are deemed useful, and how this influences system usage. VLSs have a suite of tools with associated functions/features and properties, as well as non-functional system characteristics. In addition, these systems incorporate pedagogic features to cater for online teaching. Educators in higher education, who are the chief agents of e-learning, are confounded by system-related, pedagogic, organisational, user difference and demographic factors that influence VLS usage. Virtual learning system usage involves system feature usage extent and frequency, total system usage and usage clusters. The aim of this study is to develop a model representing the factors that influence usage of VLSs in higher education. The links between system usage and system-related factors, pedagogic factors, organisational factors, user-difference and demographic factors is researched. This research incorporated a literature study, a pilot study, interviews and surveys. A case study research strategy was combined with a mixed methods research design. The results of the qualitative analysis was triangulated with the findings of the quantitative analysis and compared to the findings of the literature study. The study was conducted at two residential higher education institutions (HEI), namely, University of KwaZulu-Natal and Durban University of Technology. The main contribution of this study is the Virtual Learning System Usage Model (VLSUM) representing the factors that influence VLS usage in residential higher education institutions. The proposed VLSUM is based on the empirical results of this study. VLSUM can be used by managers of educational technology departments and instructional designers to implement interventions to optimize usage. The constructs of VLSUM confirmed existing theories, replicated and synthesised theories from different fields, and extended existing models to produce a new model for understanding the factors that influence VLS usage in higher education. / Computing / D. LITT. et. Phil. (Information Systems)