A computer-based holistic approach to managing progress of distributed agile teams

One of the co-ordination difficulties of remote agile teamwork is managing the progress of development. Several technical factors affect agile development progress; hence, their impact on progress needs to be explicitly identified and co-ordinated. These factors include source code versioning, unit testing (UT), acceptance testing (AT), continuous integration (CI), and releasing. These factors play a role in determining whether software produced for a user story (i.e. feature or use case) is ‘working software’ (i.e. the user story is complete) or not. One of the principles introduced by the Agile Manifesto is that working software is the primary measure of progress. In distributed agile teams, informal methods, such as video-conference meetings, can be used to raise the awareness of how the technical factors affect development progress. However, with infrequent communications, it is difficult to understand how the work of one team member at one site influences the work progress of another team member at a different site. Furthermore, formal methods, such as agile project management tools are widely used to support managing progress of distributed agile projects. However, these tools rely on team members’ perceptions in understanding change in progress. Identifying and co-ordinating the impact of technical factors on development progress are not considered. This thesis supports the effective management of progress by providing a computer-based holistic approach to managing development progress that aims to explicitly identify and co-ordinate the effects of the various technical factors on progress. The holistic approach requires analysis of how the technical factors cause change in progress. With each progress change event, the co-ordination support necessary to manage the event has been explicitly identified. The holistic approach also requires designing computer-based mechanisms that take into consideration the impact of technical factors on progress. A progress tracking system has been designed that keeps track of the impact of the technical factors by placing them under control of the tracking system. This has been achieved by integrating the versioning functionality into the progress tracking system and linking the UT tool, AT tool and CI tool with the progress tracking system. The approach has been evaluated through practical scenarios and has validated these through a research prototype. The result shows that the holistic approach is achievable and helps raise awareness of distributed agile teams regarding the change in the progress, as soon as it occurs. It overcomes the limitations of the informal and formal methods. Team members will no longer need to spend time determining how their change will impact the work of the other team members so that they can notify the affected members regarding the change. They will be provided with a system that helps them achieve this as they carry out their technical activities. In addition, they will not rely on static information about progress registered in a progress tracking system, but will be updated continuously with relevant information about progress changes occurring to their work

Patterns of allergic rhinitis among adults in Qassim region, Saudi Arabia: a cross sectional study

Introduction: developing and developed countries have a high prevalence of allergic rhinitis (AR). Severe AR has negative impacts on sleep, quality of life, and work performance. The study aimed to identify the patterns of AR among patients attending the ears nose and throat Unit (ENT) clinic at King Saud Hospital, Qassim, Saudi Arabia. Methods: this study was a cross-sectional study conducted at the ENT clinic of King Saudi Hospital, Unaizah City, Qassim region, Saudi Arabia. We examined outpatients diagnosed with AR using an interview questionnaire and clinical examination. Results: the sample included 455 patients. Of these, 23.7% were 21-30 years old, 65.7% had a family history of AR, 57.8% had no general symptoms, 75.6% reported runny nose as the most common nasal symptom, and 35.4% reported no complications. Dust was the most common trigger of AR (82.4%), 49.2% reported allergic symptoms in all seasons, 96% of patients have inferior turbinate hypertrophy, and oral histamine was the most commonly used treatment (33.2%). Conclusion: perineal AR and inferior turbinate hypertrophy were very common findings comparing to previous studies, further studies to assess the risk factors are highly recommended

Collaborative Crowdsourced Software Testing

Crowdsourced software testing (CST) uses a crowd of testers to conduct software testing. Currently, the microtasking model is used in CST; in it, a testing task is sent to individual testers who work separately from each other. Several studies mentioned that the quality of test reports produced by individuals was a drawback because a large number of invalid defects were submitted. Additionally, individual workers tended to catch the simple defects, not those with high complexity. This research explored the effect of having pairs of collaborating testers working together to produce one final test report. We conducted an experiment with 75 workers to measure the effect of this approach in terms of (1) the total number of unique valid defects detected, (2) the total number of invalid defects reported, and (3) the possibility of detecting more difficult defects. The findings show that testers who worked in collaborating pairs can be as effective in detecting defects as an individual worker; the differences between them are marginal. However, CST significantly affects the quality of test reports submitted in two dimensions: it helps reduce the number of invalid defects and also helps detect more difficult defects. The findings are promising and suggest that CST platforms can benefit from new mechanisms that allow for the formation of teams of two individuals who can participate in doing testing jobs

Leveraging Social Network Analysis for Crowdsourced Software Engineering Research

Crowdsourced software engineering (CSE) is an emerging area that has been gaining much attention in the last few years. It refers to the use of crowdsourcing techniques in software engineering activities, including requirements engineering, implementation, design, testing, and verification. CSE is an alternative to traditional software engineering and uses an open call to which online developers can respond to and obtain work on various tasks, as opposed to the assigning of tasks to in-house developers. The great benefits of CSE have attracted the attention of many researchers, and many studies have recently been carried out in the field. This research aims to analyze publications on CSE using social network analysis (SNA). A total of 509 CSE publications from six popular databases were analyzed to determine the characteristics of the collaborative networks of co-authorship of the research (i.e., the co-authors, institutions involved in co-authorship, and countries involved in co-authorship) and of the citation networks on which the publications of the studies are listed. The findings help identify CSE research productivity, trends, performances, community structures, and relationships between various collaborative patterns to provide a more complete picture of CSE research

Leveraging Social Network Analysis for Crowdsourced Software Engineering Research

Crowdsourced software engineering (CSE) is an emerging area that has been gaining much attention in the last few years. It refers to the use of crowdsourcing techniques in software engineering activities, including requirements engineering, implementation, design, testing, and verification. CSE is an alternative to traditional software engineering and uses an open call to which online developers can respond to and obtain work on various tasks, as opposed to the assigning of tasks to in-house developers. The great benefits of CSE have attracted the attention of many researchers, and many studies have recently been carried out in the field. This research aims to analyze publications on CSE using social network analysis (SNA). A total of 509 CSE publications from six popular databases were analyzed to determine the characteristics of the collaborative networks of co-authorship of the research (i.e., the co-authors, institutions involved in co-authorship, and countries involved in co-authorship) and of the citation networks on which the publications of the studies are listed. The findings help identify CSE research productivity, trends, performances, community structures, and relationships between various collaborative patterns to provide a more complete picture of CSE research

Identifying Users and Developers of Mobile Apps in Social Network Crowd

In the last fifteen years, an immense expansion has been witnessed in mobile app usage and production. The intense competition in the tech sector and also the rapidly and constantly evolving user requirements have led to increased burden on mobile app creators. Nowadays, fulfilling users’ expectations cannot be readily achieved and new and unconventional approaches are needed to permit an interested crowd of users to contribute in the introduction of creative mobile apps. Indeed, users and developers of mobile apps are the most influential candidates to engage in any of the requirements engineering activities. The place where both can best be found is on Twitter, one of the most widely used social media platforms. More interestingly, Twitter is considered as a fertile ground for textual content generated by the crowd that can assist in building robust predictive classification models using machine learning (ML) and natural language processing (NLP) techniques. Therefore, in this study, we have built two classification models that can identify mobile apps users and developers using tweets. A thorough empirical comparison of different feature extraction techniques and machine learning classification algorithms were experimented with to find the best-performing mobile app user and developer classifiers. The results revealed that for mobile app user classification, the highest accuracy achieved was ≈0.86, produced via logistic regression (LR) using Term Frequency Inverse Document Frequency (TF-IDF) with N-gram (unigram, bigram and trigram), and the highest precision was ≈0.86, produced via LR using Bag-of-Words (BOW) with N-gram (unigram and bigram). On the other hand, for mobile app developer classification, the highest accuracy achieved was ≈0.87, produced by random forest (RF) using BOW with N-gram (unigram and bigram), and the highest precision was ≈0.88, produced by multi-layer perception neural network (MLP NN) using BERTweet for feature extraction. According to the results, we believe that the developed classification models are efficient and can assist in identifying mobile app users and developers from tweets. Moreover, we envision that our models can be harnessed as a crowd selection approach for crowdsourcing requirements engineering activities to enhance and design inventive and satisfying mobile apps

Toward an Agile Approach to Managing the Effect of Requirements on Software Architecture during Global Software Development

Requirement change management (RCM) is a critical activity during software development because poor RCM results in occurrence of defects, thereby resulting in software failure. To achieve RCM, efficient impact analysis is mandatory. A common repository is a good approach to maintain changed requirements, reusing and reducing effort. Thus, a better approach is needed to tailor knowledge for better change management of requirements and architecture during global software development (GSD).The objective of this research is to introduce an innovative approach for handling requirements and architecture changes simultaneously during global software development. The approach makes use of Case-Based Reasoning (CBR) and agile practices. Agile practices make our approach iterative, whereas CBR stores requirements and makes them reusable. Twin Peaks is our base model, meaning that requirements and architecture are handled simultaneously. For this research, grounded theory has been applied; similarly, interviews from domain experts were conducted. Interview and literature transcripts formed the basis of data collection in grounded theory. Physical saturation of theory has been achieved through a published case study and developed tool. Expert reviews and statistical analysis have been used for evaluation. The proposed approach resulted in effective change management of requirements and architecture simultaneously during global software development

رقابة قضاء المظالم على أعمال الإدارة في المملكة العربية السعودية = Grievances judiciary supervision of administrative works in Saudi Arabia and differentiating it from the grievances judiciary supervision in the Islamic law

Jurists of administrative law consider judicial Supervision of administrative works and their actions one of the means of preserving the principle of legitimacy, which the principle that preserves the rights of individuals as well as the organization’s stability. In this context, this study attempts to shed light upon the applied general principles through which the Bureau of Grievances in the Kingdom of Saudi Arabia exercises its Supervision of management activities reflected by the implementation of the systems and rules and operating public facilities. This will be accomplished through a demonstration of the tasks of administrative courts that work under the Bureau of Grievances in its three sections: the supreme administrative court, administrative courts of appeal, and administrative courts (courts of first instance), followed by a highlight of the limits of loyalty scope of the Supervision of the Saudi administrative judiciary of administrative activities. This method will be followed by an attempt to explain texts and revealing the vague ones and benefiting from the judicial rules as practical instances. The study concludes by illustrating cases the researcher believes to be more important for differentiating grievances judiciary in Islamic system from grievances of judiciary in the Saudi system. The study is concluded by a conclusion that summarizes the research and furnishes some suggestions about its findings

Impact of Optimal Feature Selection Using Hybrid Method for a Multiclass Problem in Cross Project Defect Prediction

The objective of cross-project defect prediction (CPDP) is to develop a model that trains bugs on current source projects and predicts defects of target projects. Due to the complexity of projects, CPDP is a challenging task, and the precision estimated is not always trustworthy. Our goal is to predict the bugs in the new projects by training our model on the current projects for cross-projects to save time, cost, and effort. We used experimental research and the type of research is explanatory. Our research method is controlled experimentation, for which our independent variable is prediction accuracy and dependent variables are hyper-parameters which include learning rate, epochs, and dense layers of neural networks. Our research approach is quantitative as the dataset is quantitative. The design of our research is 1F1T (1 factor and 1 treatment). To obtain the results, we first carried out exploratory data analysis (EDA). Using EDA, we found that the dataset is multi-class. The dataset contains 11 different projects consisting of 28 different versions of all the projects in total. We also found that the dataset has significant issues of noise, class imbalance, and distribution gaps between different projects. We pre-processed the dataset for experimentation by resolving all these issues. To resolve the issue of noise, we removed duplication from the dataset by removing redundant rows. We then covered the data distribution gap between different sources and target projects using the min-max normalization technique. After covering the data distribution gap, we generated synthetic data using a CTGANsynthesizer to solve class imbalance issues. We solved the class imbalance issue by generating an equal number of instances, as well as an equal number of output classes. After carrying out all of these steps, we obtained normalized data. We applied the hybrid feature selection technique on the pre-processed data to optimize the feature set. We obtained significant results of an average AUC of 75.98%. From the empirical study, it was demonstrated that feature selection and hyper-parameter tuning have a significant impact on defect prediction accuracy in cross-projects