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Software agents, both human and computational, do not exist in isolation and often need to collaborate or coordinate with others to achieve their goals. In human society, social mechanisms such as norms ensure efficient functioning, and these techniques have been adopted by researchers in multi-agent systems (MAS) to create socially aware agents. However, traditional techniques have limitations, such as operating in limited environments often using brittle symbolic reasoning. The advent of Large Language Models (LLMs) offers a promising solution, providing a rich and expressive vocabulary for norms and enabling norm-capable agents that can perform a range of tasks such as norm discovery, normative reasoning and decision-making. This paper examines the potential of LLM-based agents to acquire normative capabilities, drawing on recent Natural Language Processing (NLP) and LLM research. We present our vision for creating normative LLM agents. In particular, we discuss how the recently proposed "LLM agent" approaches can be extended to implement such normative LLM agents. We also highlight challenges in this emerging field. This paper thus aims to foster collaboration between MAS, NLP and LLM researchers in order to advance the field of normative agents., Comment: 15 pages, 1 figure, presented at the COINE 2024 workshop at AAMAS 2024 (https://coin-workshop.github.io/coine-2024-auckland/accepted_papers.html). This paper will appear in the post-proceedings of the COINE-2024 workshop

Norms are an important component of the social fabric of society by prescribing expected behaviour. In Multi-Agent Systems (MAS), agents interacting within a society are equipped to possess social capabilities such as reasoning about norms and trust. Norms have long been of interest within the Normative Multi-Agent Systems community with researchers studying topics such as norm emergence, norm violation detection and sanctioning. However, these studies have some limitations: they are often limited to simple domains, norms have been represented using a variety of representations with no standard approach emerging, and the symbolic reasoning mechanisms generally used may suffer from a lack of extensibility and robustness. In contrast, Large Language Models (LLMs) offer opportunities to discover and reason about norms across a large range of social situations. This paper evaluates the capability of LLMs to detecting norm violations. Based on simulated data from 80 stories in a household context, with varying complexities, we investigated whether 10 norms are violated. For our evaluations we first obtained the ground truth from three human evaluators for each story. Then, the majority result was compared against the results from three well-known LLM models (Llama 2 7B, Mixtral 7B and ChatGPT-4). Our results show the promise of ChatGPT-4 for detecting norm violations, with Mixtral some distance behind. Also, we identify areas where these models perform poorly and discuss implications for future work., Comment: To appear in COINE@AAMAS-2024 Springer LNCS post-proceedings

Social inclusion is a fundamental feature of thriving societies. This paper first investigates barriers for social inclusion in online Software Engineering (SE) communities, by identifying a set of 11 attributes and organising them as a taxonomy. Second, by applying the taxonomy and analysing language used in the comments posted by members in 189 Gitter projects (with > 3 million comments), it presents the evidence for the social exclusion problem. It employs a keyword-based search approach for this purpose. Third, it presents a framework for improving social inclusion in SE communities., Comment: 6 pages, 5 figures, this paper has been accepted to the short paper track of EASE 2023 conference (see https://conf.researchr.org/track/ease-2023/ease-2023-short-papers-and-posters#event-overview)

Information technology and software services are pervasive, occupying the centre of most aspects of contemporary societies. This has given rise to commonly expected norms and expectations around how such systems should work, appropriate penalties for violating these expectations, and more importantly, indicators of how to reduce the consequences of violations and sanctions. Evidence for expectation violations and ensuing sanctions exists in a range of portals used by individuals and groups to start new friendships, explore new ideas, and provide feedback for products and services. Therein lies insights that could lead to functional socio-technical systems, and general awareness and anticipations of human actions (and interactions) when using information technology and software services. However, limited previous work has examined such artifacts to provide these understandings. To contribute to such understandings and theoretical advancement we study expectation violations in mobile apps, considered among the most engaging socio-technical systems. We used content analysis and expectancy violation theory (EVT) and expectation confirmation theory (ECT) to explore the evidence and nature of sanctions in app reviews for a specific domain of apps. Our outcomes show that users respond to expectation violation with sanctions when their app does not work as anticipated, developers seem to target specific market niches when providing services in an app domain, and users within an app domain respond with similar sanctions. We contribute to the advancement of expectation violation theories, and we provide practical insights for the mobile app community., Comment: 32 pages, 4 figures, 8 tables

Software Engineering (SE) communities such as Stack Overflow have become unwelcoming, particularly through members' use of offensive language. Research has shown that offensive language drives users away from active engagement within these platforms. This work aims to explore this issue more broadly by investigating the nature of offensive language in comments posted by users in four prominent SE platforms - GitHub, Gitter, Slack and Stack Overflow (SO). It proposes an approach to detect and classify offensive language in SE communities by adopting natural language processing and deep learning techniques. Further, a Conflict Reduction System (CRS), which identifies offence and then suggests what changes could be made to minimize offence has been proposed. Beyond showing the prevalence of offensive language in over 1 million comments from four different communities which ranges from 0.07% to 0.43%, our results show promise in successful detection and classification of such language. The CRS system has the potential to drastically reduce manual moderation efforts to detect and reduce offence in SE communities.

Governance has been highlighted as a key factor in the success of an Open Source Software (OSS) project. It is generally seen that in a mixed meritocracy and autocracy governance model, the decision-making (DM) responsibility regarding what features are included in the OSS is shared among members from select roles; prominently the project leader. However, less examination has been made whether members from these roles are also prominent in DM discussions and how decisions are made, to show they play an integral role in the success of the project. We believe that to establish their influence, it is necessary to examine not only discussions of proposals in which the project leader makes the decisions, but also those where others make the decisions. Therefore, in this study, we examine the prominence of members performing different roles in: (i) making decisions, (ii) performing certain social roles in DM discussions (e.g., discussion starters), (iii) contributing to the OSS development social network through DM discussions, and (iv) how decisions are made under both scenarios. We examine these aspects in the evolution of the well-known Python project. We carried out a data-driven longitudinal study of their email communication spanning 20 years, comprising about 1.5 million emails. These emails contain decisions for 466 Python Enhancement Proposals (PEPs) that document the language's evolution. Our findings make the influence of different roles transparent to future (new) members, other stakeholders, and more broadly, to the OSS research community.

Text mining approaches are being used increasingly for business analytics. In particular, such approaches are now central to understanding users' feedback regarding systems delivered via online application distribution platforms such as Google Play. In such settings, large volumes of reviews of potentially numerous apps and systems means that it is infeasible to use manual mechanisms to extract insights and knowledge that could inform product improvement. In this context of identifying software system improvement options, text mining techniques are used to reveal the features that are mentioned most often as being in need of correction (e.g., GPS), and topics that are associated with features perceived as being defective (e.g., inaccuracy of GPS). Other approaches may supplement such techniques to provide further insights for online communities and solution providers. In this work we augment text mining approaches with social network analysis to demonstrate the utility of using multiple techniques. Our outcomes suggest that text mining approaches may indeed be supplemented with other methods to deliver a broader range of insights., Comment: Conference paper, 9 pages, 2 figures, 5 tables

It is known that user involvement and user-centered design enhance system acceptance, particularly when end-users' views are considered early in the process. However, the increasingly common method of system deployment, through frequent releases via an online application distribution platform, relies more on post-release feedback from a virtual community. Such feedback may be received from large and diverse communities of users, posing challenges to developers in terms of extracting and identifying the most pressing requests to address. In seeking to tackle these challenges we have used natural language processing techniques to study enhancement requests logged by the Android community. We observe that features associated with a specific subset of topics were most frequently requested for improvement, and that end-users expressed particular discontent with the Jellybean release. End-users also tended to request improvements to specific issues together, potentially posing a prioritization challenge to Google., Comment: Conference Proceeding, 10 pages, 4 figures, 3 tables

A sound Decision-Making (DM) process is key to the successful governance of software projects. In many Open Source Software Development (OSSD) communities, DM processes lie buried amongst vast amounts of publicly available data. Hidden within this data lie the rationale for decisions that led to the evolution and maintenance of software products. While there have been some efforts to extract DM processes from publicly available data, the rationale behind how the decisions are made have seldom been explored. Extracting the rationale for these decisions can facilitate transparency (by making them known), and also promote accountability on the part of decision-makers. This work bridges this gap by means of a large-scale study that unearths the rationale behind decisions from Python development email archives comprising about 1.5 million emails. This paper makes two main contributions. First, it makes a knowledge contribution by unearthing and presenting the rationale behind decisions made. Second, it makes a methodological contribution by presenting a heuristics-based rationale extraction system called Rationale Miner that employs multiple heuristics, and follows a data-driven, bottom-up approach to infer the rationale behind specific decisions (e.g., whether a new module is implemented based on core developer consensus or benevolent dictator's pronouncement). Our approach can be applied to extract rationale in other OSSD communities that have similar governance structures., Comment: 12 pages, 5 figures, 3 tables, appears in the proceedings of the 43rd International Conference on Software Engineering (ICSE 2021)

Platforms such as Stack Overflow are available for software practitioners to solicit solutions to their challenges and knowledge needs. The practices therein have in recent times however triggered quality related concerns. This is a noteworthy issue when considering that the Stack Overflow platform is used by numerous software developers. Academic research tends to provide validation for the practices and processes employed by Stack Overflow and other such forums. However, previous work did not review the scale of scientific attention that is given to this cause. Continuing from our preliminary work, we conducted a Systematic Mapping study involving 265 papers from six relevant databases to address this gap. In this work, we explored the level of academic interest Stack Overflow has generated, the publication venues that are targeted, the topics that are studied, approaches used, types of contributions and the quality of the publications that are written about Stack Overflow. Outcomes show that Stack Overflow has attracted increasing research interest over the years, with topics relating to both community dynamics and human factors, and technical issues. In addition, research studies have been largely evaluative or proposed solutions; however, the latter approach tends to lack validation. The contributions of these studies are often techniques or answers to a specific problem. Evaluating the quality of all studies that were dedicated to software programming (58 papers), our outcomes show that on average only 58% of the developed quality criteria were met. Notwithstanding that research is continually aiming to understand Stack Overflow and other similar communities, further investigations are required to validate such studies and the solutions they propose., Comment: 38 pages, 5 figures

This paper investigates the impact of changes in agents' beliefs coupled with dynamics in agents' meta-roles on the evolution of institutions. The study embeds agents' meta-roles in the BDI architecture. In this context, the study scrutinises the impact of cognitive dissonance in agents due to unfairness of institutions. To showcase our model, two historical long-distance trading societies, namely Armenian merchants of New-Julfa and the English East India Company are simulated. Results show how change in roles of agents coupled with specific institutional characteristics leads to changes of the rules in the system., Comment: arXiv admin note: text overlap with arXiv:2004.11858

This paper presents a conceptual refinement of agent cognitive architecture inspired from the beliefs-desires-intentions (BDI) and the theory of planned behaviour (TPB) models, with an emphasis on different belief facets. This enables us to investigate the impact of personality and the way that an agent weights its internal beliefs and social sanctions on an agent's actions. The study also uses the concept of cognitive dissonance associated with the fairness of institutions to investigate the agents' behaviour. To showcase our model, we simulate two historical long-distance trading societies, namely Armenian merchants of New-Julfa and the English East India Company. The results demonstrate the importance of internal beliefs of agents as a pivotal aspect for following institutional rules., Comment: Submitted to COINE 2020 workshop

Norms are behavioral expectations in communities. Online communities are also expected to abide by the rules and regulations that are expressed in the code of conduct of a system. Even though community authorities continuously prompt their users to follow the regulations, it is observed that hate speech and abusive language usage are on the rise. In this paper, we quantify and analyze the patterns of violations of normative behaviour among the users of Stack Overflow (SO) - a well-known technical question-answer site for professionals and enthusiast programmers, while posting a comment. Even though the site has been dedicated to technical problem solving and debugging, hate speech as well as posting offensive comments make the community "toxic". By identifying and minimising various patterns of norm violations in different SO communities, the community would become less toxic and thereby the community can engage more effectively in its goal of knowledge sharing. Moreover, through automatic detection of such comments, the authors can be warned by the moderators, so that it is less likely to be repeated, thereby the reputation of the site and community can be improved. Based on the comments extracted from two different data sources on SO, this work first presents a taxonomy of norms that are violated. Second, it demonstrates the sanctions for certain norm violations. Third, it proposes a recommendation system that can be used to warn users that they are about to violate a norm. This can help achieve norm adherence in online communities., Comment: 16 pages, 8 figures, 2 tables

This research aims to design and develop a new requirements prioritization approach for analyzing and prioritizing stakeholders requirements which are mentioned in the feedback for software products. This paper presents a PhD research agenda and preliminary outcomes from the early analysis. A roadmap to the proposed research methodology that is to be followed to achieve the targeted outcomes is also outlined. Outcomes to date show that the requirements prioritization problem has been researched extensively, however, gaps still remain when considering techniques that handle a large number of crowdsourced requirements. Furthermore, requirements prioritization as a problem affects many domains beyond software engineering. Hence, knowledge from other fields could be useful for informing requirements prioritization practice in the software engineering space.

Enterprises are always on the lookout for tools that analyze end-users perspectives on their products. In particular, app reviews have been assessed as useful for guiding improvement efforts and software evolution, however, developers find reading app reviews to be a labor intensive exercise. If such a barrier is eliminated, however, evidence shows that responding to reviews enhances end-users satisfaction and contributes towards the success of products. In this paper, we present Appsent, a mobile analytics tool as an app, to facilitate the analysis of app reviews. This development was led by a literature review on the problem and subsequent evaluation of current available solutions to this challenge. Our investigation found that there was scope to extend currently available tools that analyze app reviews. These gaps thus informed the design and development of Appsent. We subsequently performed an empirical evaluation to validate Appsent usability and the helpfulness of analytics features from users perspective. Outcomes of this evaluation reveal that Appsent provides user-friendly interfaces, helpful functionalities and meaningful analytics. Appsent extracts and visualizes important perceptions from end-users feedback, identifying insights into end-users opinions about various aspects of software features. Although Appsent was developed as a prototype for analyzing app reviews, this tool may be of utility for analyzing product reviews more generally.