Software engineering is a comprehensive process that requires developers and team members to collaborate across multiple tasks. In software testing, bug triaging is a tedious and time-consuming process. Assigning bugs to the appropriate developers can save time and maintain their motivation. However, without knowledge about a bug’s class, triaging is difficult. Motivated by this challenge, this paper focuses on the problem of assigning a suitable developer to a new bug by analyzing the history of developers’ profiles and analyzing the history of bugs for all developers using machine learning-based recommender systems. Explainable AI (XAI) is AI that humans can understand. It contrasts with “black box” AI, which even its designers cannot explain. By providing appropriate explanations for results, users can better comprehend the underlying insight behind the outcomes, boosting the recommender system’s effectiveness, transparency, and confidence. The trained model is utilized in the recommendation stage to calculate relevance scores for developers based on expertise and past bug handling performance, ultimately presenting the developers with the highest scores as recommendations for new bugs. This approach aims to strike a balance between computational efficiency and accurate predictions, enabling efficient bug assignment while considering developer expertise and historical performance. In this paper, we propose two explainable models for recommendation. The first is an explainable recommender model for personalized developers generated from bug history to know what the preferred type of bug is for each developer. The second model is an explainable recommender model based on bugs to identify the most suitable developer for each bug from bug history.

As security breaches are increasingly widely reported in today’s culture, cybersecurity is gaining attention on a global scale. Threat modeling methods (TMM) are a proactive security practice that is essential for pinpointing risks and limiting their impact. This paper proposes a hybrid threat modeling framework based on system-centric, attacker-centric, and risk-centric approaches to identify threats in Operational Technology (OT) applications. OT is made up of software and hardware used to manage, secure, and control industrial control systems (ICS), and its environments include factories, power plants, oil and gas refineries, and pipelines. To visualize the “big picture” of its infrastructure risk profile and improve understanding of the full attack surface, the proposed framework builds on several threat modeling methodologies: PASTA modeling, STRIDE, and attack tree components. Nevertheless, the continuity and stability of vital infrastructure will continue to depend heavily on legacy equipment. Thus, protecting the availability, security, and safety of industrial environments and vital infrastructure from cyberattacks requires operational technology (OT) cybersecurity. The feasibility of the proposed approach is illustrated with a case study from a real oil and gas production plant control system where numerous significant cyberattacks in recent years have targeted OT networks more frequently as hackers realized the possibility of disruption due to insufficient OT security, particularly for outdated systems. The proposed framework achieved better results in detecting threats and severity in the design of the case study system, helping to increase security and support cybersecurity assessment of legacy control systems.

Teleworking refers to the utilization of information and communication technologies for work done outside the workplace. The Covid-19 crisis led to increased utilisation of social networking tools within enterprises, especially when working remotely. The aim of their use is often to improve situational awareness, coordination, and collaboration amongst employees. Online social transparency, typically done through social networks or enterprise social software, refers to the voluntary sharing of personal and contextual information such as those relating to their own and team status, intentions, motivation, capabilities, goal priorities besides updates on the physical and social context, with other colleagues. An ad-hoc practice of social transparency can introduce risks such as information overload, social loafing and peer pressure. Despite recognising its adverse effects, there is a lack of systematic methods that identify and assess the risks of online social transparency. In this paper, we present a method to identify and evaluate these within enterprises. We present the method’s workflow, stakeholders, the novel artefacts and techniques devised to use and the outcomes to produce. We evaluate our proposed method by applying it in a real organisational context and assess applicability, efficiency, and effectiveness in identifying risks and supporting managers in risk assessment. The results showed that the method gives a framework of thinking and analysis and helps recognize and identify risks in a specialized manner.

Gamification has become a motivational technique for enhancing engagement and productivity, extending into agile software development. However, integrating gamification into agile frameworks such as the Scrum framework has led to the emergence of gamification risks, which may have adverse impacts on agile roles and tasks. These risks include an increase in the number of unassigned tasks affecting sprint velocity, novelty-seeking and quick boredom, clustering group, and intimidation, thus showing the need for a structured approach toward their management, their impacts on team dynamics and project outcomes. This study proposes the Agile Gamification Risk Management (AGRM) process, focused on identifying, assessing, and mitigating gamification risks in agile software enterprises. AGRM introduces artifacts such as the Gamification Risk Reporting Form, Personalized Risk Profiles, Task Impact Matrix, and Gamification Risk Register, enabling real-time proactive risk management. By leveraging a gamification risk taxonomy, AGRM categorizes and prioritizes risks, aligning mitigation efforts effectively. This paper details a two-phased empirical study to evaluate our proposed AGRM process. The proposed process identified 17 and mitigated 9 gamification risks for two agile teams in two software development enterprises. Unlike ad hoc practices, AGRM provides a structured approach, empowering teams to manage risks during sprint events. By incorporating artifacts like the Gamification Risk Register (GRR) and Personalized Risk Profiles (PRPs), teams can assess risks in context, enhancing productivity, collaboration, and project outcomes. The results demonstrate AGRM’s ability to boost team morale and confidence in addressing gamification risks effectively.

Gamification, which is the use of game elements in a non-game context, has become a trend in many industries. It is used as means for motivating and engaging users and influencing their behaviors. Thus, during the last few years many researchers have attempted to find a way to design and implement gamified systems that are adaptively personalized based on user types. This paper proposes a framework that aims to aid software engineers in systematically designing adaptively personalized gamification applications of any context type by adapting the appearance of gamification elements based on each user type. The framework introduces a method that provides a systematic means to modify the system at runtime (i.e. while the system is in use) based on the user’s preferences and behavior by changing the existing elements based on the usage of each user. We validate the proposed framework using a case study conducted with employees of a large-scale software development company. By applying all their practical comments, the methods of the proposed framework were enhanced. Moreover, to formalize the proposed framework we develop an ontology that implements the mappings between game elements and user types by defining rules that govern their usage. In this paper, an ontology validation is presented including the use of Reasoner, Instances, SPARQL queries, and requirements model implementation. Also, a sample prototype is presented on one of the most used application and show how we apply this framework on it. Received: 13 June 2023 Accepted: 16 September 2023 Published: 20 October 2023