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Mass production has attracted much attention as a new approach to knowledge production. The R software system is a typical product of mass production. For its unique architecture, the R software system accurately recorded the natural process of knowledge propagation and inheritance. Thus, this article established a dynamic complex network model based on the derivative relationship between R software packages, which reflects the evolution process of online knowledge production structure in R software system, and studied the process of knowledge propagation and inheritance via the dynamic complex network analysis method. These results show that the network size increases with time, reflecting the tendency of R software to accelerate the accumulation of knowledge. The network density and network cohesion decrease with the increase of scale, indicating that the knowledge structure of R software presents a trend of expansion. The unique extension structure of R software provides a rich research foundation for the propagation of knowledge; thus, the results can provide us a new perspective for knowledge discovery and technological innovation.

The use of software in mission critical applications poses greater quality needs. Quality assurance activities are aimed at ensuring such quality requirements of the software system. Antifragility is a property of software that increases its quality as a result of errors, faults, and attacks. Such antifragile software systems proactively accepts the errors and learns from these errors and relies on test-driven development methodology. In this article, an innovative approach is proposed which uses a fault injection methodology to perform the task of quality assurance. Such a fault injection mechanism makes the software antifragile and it gets better with the increase in the intensity of such errors up to a point. A software quality game is designed as a two-player game model with stressor and backer entities. The stressor is an error model which injects errors into the software system. The software system acts as a backer, and tries to recover from the errors. The backer uses a cheating mechanism by implementing software Learning Hooks (SLH) which learn from the injected errors. This makes the software antifragile and leads to improvement of the code. Moreover, the SLH uses a Q-Learning reinforcement algorithm with a hybrid reward function to learn from the incoming defects. The game is played for a maximum of K errors. This approach is introduced to incorporate the anti-fragility aspects into the software system within the existing framework of object-oriented development. The game is run at the end of every increment during the construction of object-oriented systems. A detailed report of the injected errors and the actions taken is output at the end of each increment so that necessary actions are incorporated into the actual software during the next iteration. This ensures at the end of all the iterations, the software is immune to majority of the so-called Black Swans. The experiment is conducted with an open source Java sample and the results are studied selected two categories of evaluation parameters. The defect related performance parameters considered are the defect density, defect distribution over different iterations, and number of hooks inserted. These parameters show much reduction in adopting the proposed approach. The quality parameters such as abstraction, inheritance, and coupling are studied for various iterations and this approach ensures considerable increases in these parameters.

What is software architecture? A clear and simple definition is that software architecture is about making important design decisions that you want to get right early in the development of a software system because, in the future, they are costly to change. Being a good software architect is not easy. It requires not only a deep technical competency from practicing software architecture design in industry, but also an excellent understanding of the theoretical foundations of software architecture are gained from doing software architecture research. This chapter describes some significant research, development, and education activities that the author has performed during her professional trajectory path to develop knowledge, skills, and experiences around this topic.

Security of the software system is a prime focus area for software development teams. This paper explores some data science methods to build a knowledge management system that can assist the software development team to ensure a secure software system is being developed. Various approaches in this context are explored using data of insurance domain-based software development. These approaches will facilitate an easy understanding of the practical challenges associated with actual-world implementation. This paper also discusses the capabilities of language modeling and its role in the knowledge system. The source code is modeled to build a deep software security analysis model. The proposed model can help software engineers build secure software by assessing the software security during software development time. Extensive experiments show that the proposed models can efficiently explore the software language modeling capabilities to classify software systems’ security vulnerabilities.

Pedagogical patterns describe teaching ideas that can be applied in different ways in teaching in different disciplines, and for different types of students. They are a tool for sharing experience and good practices between teachers. The use of pedagogical patterns in online learning is a challenge that can be met by using an appropriate software system for learning management. The article proposes a model of educational objects, suitable for software imple-mentation, which we call pedagogical pattern instances, or for short - in-stances. One instance combines specific learning content with additional fea-tures. Learning content can have different “views” that present knowledge in different ways, for example, through text files of presentations, audio or video content, interactive content, etc. Logical categories of characteristics and activities form the “aspects” of the instance, such as methodology, adap-tivity, assessment, etc. The proposed pattern instance model is flexible. It can be expanded with new features and adapted to specific goals and designs. The paper also outlines a conceptual framework of an e-learning software system using the presented model of a pattern instance.

In this project was shown software system for drug search in online mode, which was included: three services, user interface. Also, was analyzed test of this system, speed for system and efficiency of system. A software system – this is a group of integrated software tools which support certain process and share a database. This system shows how three services work: the primary service, the additional service and provider’s service. Also given are the modes of their work, their capabilities and specifically what they include and represent. The description of all technologies are given which used during development of this system, including their advantages and disadvantages in general. On the basic of this technique, the client-server system of drug search is realized. Moreover there are various options for searching for medical items, a searching by using search and by using location of the pharmacy on Google map were suggested. A description of the development environment and main of its functions were provided. In addition, what is REST API, where it was used and why exactly REST was used during development of this system. It also shows how to run the program, namely the procedure, with description, explains exactly how to run the program, and what additional plugins need to be installed. This article describes the algorithm of software system for drug search in online mode, the main functions of the system are given and described. Screenshots of the main modules of the server side and of the program are added as well as screenshots of databases.

Background Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users’ thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users’ insomnia and their Big 5 personality traits as derived from social media interactions. Objective The purpose of this study is to build an insomnia prediction model from users’ psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets. Methods In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users’ personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets. Results Our classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, “no,” “not,” “never”). Some people frequently use swear words (eg, “damn,” “piss,” “fuck”) with strong temperament. They also use anxious (eg, “worried,” “fearful,” “nervous”) and sad (eg, “crying,” “grief,” “sad”) words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found. Conclusions Our model can help predict insomnia from users’ social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients.