Social Software Engineering Research Articles

Leveraging Large Language Models for Automating Inductive Qualitative Coding: A Comparative Study of Prompt Engineering Techniques

This study explores the potential of Large Language Models (LLMs), like those from the GPT family, to automate inductive qualitative coding—a process of assigning codes to text excerpts and organizing them into categories, traditionally done manually in social science and software engineering research. Our primary question is: Can LLMs effectively automate inductive qualitative coding? To investigate this, we compared different prompt engineering techniques, including Zero-shot, Few-shot, and Chain-of-Thought (CoT) learning, in coding interview transcripts. While LLMs cannot fully replace human coders, they can aid the process with a human-in-the-loop approach. Few-shot learning showed consistent performance with moderate amounts data, while CoT proved most effective in reducing partial hallucinations. Initially aimed at full automation, our study pivoted to testing prompt strategies after realizing that a human-in-the-loop process would offer better accuracy and flexibility, given the challenges of context and token limits in LLMs. These findings suggest that tailored LLM with adequate prompting techniques can help assist researchers when performing qualitative analysis.

Determining the Role of Social Identity Attributes to the Protection of Users’ Privacy in Social Media

Drawing on digital identity theories, social software engineering theory (SSE), and the Privacy Safeguard (PriS) methodology, we examined the way that personal information uploaded on social media (SM) imposes privacy issues. Throughout a review on users’ self-representation on SM, we examined the impact of self-determination and self-disclosure on users’ privacy, and we identified the social attributes (SA) that cause privacy implications. This paper specifies 18 SA that users employ to achieve their optimal level of representation while summarizing possible ways that these attributes provoke users’ identification. In particular, our research has shown that SM users represent their personas by unveiling SA to construct popular, representative, and conversational profiles. As disclosing SA increases privacy implications, we intend to help users build profiles that respect their privacy. Examining users’ SA deepens our understanding of disclosing personal information on SM while leading to a better quantification of identity attributes; furthermore, users’ top five most revealing attributes were summarized. Considering that SSE addresses users’ privacy implications from an early stage of systems designing, our research, identifying the SA, will be helpful in addressing privacy from a socio-technical aspect, aiming at bridging the socio-technical gap by drawing designers’ attention to users’ social aspects.

Understanding the role of users’ socio-location attributes and their privacy implications on social media

PurposeThe purpose of this paper is to establish reciprocity among socio-location attributes while underlining the additional users’ privacy implications on social media (SM).Design/methodology/approachDigital identity theories, social software engineering theory and the Privacy Safeguard (PriS) methodology were considered while reviewing 32 papers for identifying users’ SM attributes. After proposing interrelations among socio-location attributes, the PriS method was used to match social aspects of privacy in designing case studies to illustrate the associations through potential users’ privacy implications.FindingsEighteen users’ SM attributes were collected and correlated to the Face, Frame, Activity, Time and Stage (FFrATS) 4 W (socio-location attributes), which provoke further privacy implications due to the notions of self-determination and self-disclosure on SM. The authors draw on the PriS methodology to address privacy’s multidimensionality while creating case studies to examine privacy issues arising due to socio-location attribute disclosure and users’ trajectories and normativity lines.Research limitations/implicationsSupplementary case studies and research are needed to enable the design of a socio-spatially and privacy-aware designing methodology.Practical implicationsDesigning proper methodologies and techniques to address users’ privacy implications deriving from socio-location attributes can provide designers with a technical solution to SM platforms.Social implicationsSocio-location attribute disclosure constructs representative SM profiles; however, the revelation of attributes and their interrelations create additional privacy implications for SM users.Originality/valueDeepening the understanding of disclosing socio-location attributes on SM while bridging the socio-technical gap will provide the necessary background for proposing technical solutions to protecting users’ privacy.

АБСТРАКТНЫЕ ТИПЫ ДАННЫХ ДЛЯ ПРЕДСТАВЛЕНИЯ ЗНАНИЙ И СПЕЦИФИКАЦИИ МУЛЬТИАГЕНТНЫХ СИСТЕМ

Distributed system is a group of decentralized interacting executers. Distributed algorithm is the communication protocol for a distributed system that transforms the group into a team to solve some task. Multiagent system is a distributed system that consists of autonomous reactive agents, i.e. executers which internal states can be characterized in terms Believes (B), Desires (D), and Intentions (I). Multiagent algorithm is a distributed algorithm for a multiagent system. The article discusses the basic concepts of agents and multi-agent systems. Also, two problems of multi-agent algorithms for representing knowledge in the context of Social Software Engineering are considered. A number of new multi-agent algorithms are presented, and their correctness is proved. The main characteristics of agents are provided, such as autonomy, proactivity, social ability, and reactivity; also, agents can have such additional characteristics as persistence, reasonability, performance, mobility, personality, and rationality. A number of new multi-agent algorithms are presented, and their correctness is proved. Two statements have been proved for solving RAM and MRP problems. This time we address a social issue of agent anonymity and privacy in these algo-rithms.

AutoAt: A deep autoencoder-based classification model for supervised authorship attribution

Authorship attribution is the task of determining the likely author of a given text, with applications in domains such as literature and literary history, social network analysis, software engineering and cybersecurity. AutoAt, a deep autoencoder-based classification model which exploits the ability of autoencoders to encode meaningful data patterns is proposed to solve this task. Experiments are conducted on a data set of 1571 poems authored by 8 Romanian poets using a distributed document representation. The proposed approach obtains comparable or better results with respect to other machine learning classifiers. Additionally, the formulation of the AutoAt model allows for the computation of the probability that a test instance belongs to a given author class, which may be a useful property in a variety of authorship attribution applications. This aspect and the fact that AutoAt performs well in the difficult task of authorship attribution on poetic data without the step of feature engineering being informed by domain knowledge show that the proposed classifier is a general one, with potential to be used successfully in other fields.

Захист інформаційно-комунікаційної мережі установи від несанкціонованого доступу

У статті розглянуто питання можливості проникнення до інформаційно-комунікаційної мережі установи, проаналізовано та симульовано дії реального порушника контурів захисту інформації на прикладі реальної інформаційної інфраструктури, наведено загальний порядок оцінювання та забезпечення вдосконалення систем захисту інформації шляхом проведення тестування на проникнення. Запропоновані методи проведення тестування на проникнення можуть використовуватись при розробці та тестуванні нових систем захисту інформації, а також оцінювання ефективності та вдосконалення вже існуючих систем. Результати можуть бути використані в галузі захисту інформації, зокрема в сучасних інформаційно-комунікаційних системах та мережах (ІКСМ) для оцінювання та підвищення ефективності застосування комплексних систем та окремих засобів захисту інформації в сучасних ІКСМ.

Sentiment Polarity Detection for Software Development

The role of sentiment analysis is increasingly emerging to study software developers' emotions by mining crowd-generated content within social software engineering tools. However, off-the-shelf sentiment analysis tools have been trained on non-technical domains and general-purpose social media, thus resulting in misclassifications of technical jargon and problem reports. Here, we present Senti4SD, a classifier specifically trained to support sentiment analysis in developers' communication channels. Senti4SD is trained and validated using a gold standard of Stack Overflow questions, answers, and comments manually annotated for sentiment polarity. It exploits a suite of both lexicon- and keyword-based features, as well as semantic features based on word embedding. With respect to a mainstream off-the-shelf tool, which we use as a baseline, Senti4SD reduces the misclassifications of neutral and positive posts as emotionally negative. To encourage replications, we release a lab package including the classifier, the word embedding space, and the gold standard with annotation guidelines.

Is Green Computing a Social Software Engineering Domain?

Reusing of information technological product is called Green Computing. Recycling plays an important role in Green Computing. In generally, Green Computing is the deployment, optimization, virtualization and energy management and more over resource allocation. This includes software, hardware, applications, utilities, drivers and so on and can be treated as model or approach foe eco-friendly information technology utilization. Cloud Technology and Green Computing are related with Cloud Computing. To prepare easy and effective Information Systems virtualization is the main fundamentals of Cloud Computing. Use of minimum computers and machine are recommended which provide centralized service. The aim of Green Computing may be fulfill by appropriate Cloud Computing utilization directly and indirectly. Apart from dealing with developing programmes or systems it also helps in developing a big spectrum of related components, which centralize software. Software Engineering is a common names in today's information age. A list of instruction or data loaded into the computer to do certain activities in the computer is recognized as software. Software is a list of instruction or programme to carry out function in the computer and here software engineering play an important role for the social and cultural development and for designing and development of systems. Machine and this trend is increasing rapidly. This paper is illustrated several aspects of Software Engineering including its basic as well as fundamentals and some concern deals with Software Engineering in generally as well as in the perspective of social and community product development. Paper is expressed how Green Computing is an important socially touched domain and more clearly a social software engineering field.

Semantic tagging and linking of software engineering social content

Social online communities and platforms play a significant role in the activities of software developers either as an integral part of the main activities or through complimentary knowledge and information sharing. As such techniques become more prevalent resulting in a wealth of shared information, the need to effectively organize and sift through the information becomes more important. Top-down approaches such as formal hierarchical directories have shown to lack scalability to be applicable to these circumstanes. Light-weight bottom-up techniques such as community tagging have shown promise for better organizing the available content. However, in more focused communities of practice, such as software engineering and development, community tagging can face some challenges such as tag explosion, locality of tags and interpretation differences, to name a few. To address these challenges, we propose a semantic tagging approach that benefits from the information available in Wikipedia to semantically ground the tagging process and provide a methodical approach for tagging social software engineering content. We have shown that our approach is able to provide high quality tags for social software engineering content that can be used not only for organizing such content but also for making meaningful and relevant content recommendation to the users both within a local community and also across multiple social online communities. We have empirically validated our approach through four main research questions. The results of our observations show that the proposed approach is quite effective in organizing social software engineering content and making relevant, helpful and novel content recommendations to software developers and users of social software engineering communities.

Social Software Engineering as non profit technologies: Trends and future Potentials for Social Informatics and Digital Humanities

Software Engineering and its application and integration to the community or society is called as Social Software Engineering. This is a concept and procedure rather than tools and products towards design, development and management of products, software, application and system for the society or community; directly and indirectly and thus social Software Engineering gain popularity recently for building Social Computing and Social Informatics much more advance and popular. In other sense, such Engineering practice may also known as Non Profit Technologies. This paper is talks about Social Software Engineering including its basic feature and characteristics. Paper also illustrated some technologies and products which are helps in promotion of Digital Humanities and Digital product application too.

Collaborative and social development environments: a literature review

Software developers spend a majority of their time using a development environment coupled with a variety of collaborative tools. They use integrated development environments IDEs in different editing and debugging activities while solving particular technical problems or developing different pieces of software. These programming environments evolved from command line tools, to text-based IDEs, graphical IDEs, and eventually to collaborative and social development environments SDEs. This paper reviews the current state of the art in collaborative development environments CDEs and surveys the evolution in this area. The importance of collaboration within software engineering is addressed, followed by detailed information about collaborative systems for software development. Groupware architectures, taxonomies, and groupware applications relevant to software development are discussed, and various research systems that fall within these classifications are described. The paper concludes with recent research related to collaborative and social software engineering.

Why attending conferences is important for your research

In this paper, a personal perspective on how important for a scientist is to attend conferences is given. The report is influenced by the author's attendance of the 9th joint meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE 2013). ESEC/FSE 2013 took place in Saint Petersburg, Russia and gathered researchers, practitioners, and educators from all over the world that presented and discussed the most recent work, trends, and challenges in the field of software engineering. ESEC/FSE joined the main conference, and several co-located events: two conferences, four workshops, doctoral symposium and seven tutorials. While the main conference covered broad topics in software engineering, the two co-located conferences were targeted to the specific software engineering community: multicore software engineering, performance, and tools, and to the search-based software engineering community. Additionally, there were four interesting workshops: on social software engineering, on software evolution, on software ecosystem architectures, and on software development lifecycle for mobile. Keywords Software engineering, discuss, attending conferences.

Fast layout computation of clustered networks: Algorithmic advances and experimental analysis

The visual analysis of large and complex relational data sets is a growing need in many application domains, such as social sciences, biology, computer networks, and software engineering. In this respect, the capability of quickly computing two-dimensional layouts of hierarchically clustered networks plays an important role and should be a major requirement of many graph visualization systems. We present algorithmic and experimental advances on the subject, namely: (i) we propose a new drawing algorithm that combines space-filling and fast force-directed methods; it runs in O(n logn+m) time, where n and m are the number of vertices and edges of the network, respectively. This running time does not depend on the number of clusters, thus the algorithm guarantees good time performance independently of the structure of the cluster hierarchy. As a further advantage, the algorithm can be easily parallelized. (ii) We discuss the results of an experimental analysis aimed at understanding which clustering algorithms can be used in combination with our visualization technique to generate better quality drawings for small-world and scale-free networks of medium and large size. As far as we know, no previous similar experiments have been done to this aim.

An Architecture to Support Learning, Awareness, and Transparency in Social Software Engineering

Classical tools for supporting software engineering teams (collaborative development environment, CDE) are designed to support one team during the development of a product. Often the required data sources or experts reside outside of the internal project team and thus not provided by these CDEs. This paper describes an approach for a community-embedded CDE (CCDE), which is capable of handling multiple projects of several organizations, providing inter-project knowledge sharing and developer awareness. The presented approach uses the mashup pattern to integrate multiple data sources in order to provide software teams with an exactingly development environment.