User participation and user feedback are essential to the success of new product development (NPD). Development teams use user feedback to derive requirement engineering artifacts, such as user scenarios, user stories, concept mindmaps, and user personas, to guide them in identifying and addressing a particular user problem. However, finding enough user participation to collect meaningful feedback is challenging, and less attention has been given to addressing this. In this paper, we propose Active Personas (APs), fictional users capable of generating contextual feedback through an interactive multi-modal interaction, such as text, voice, image, and video. APs enable development teams to gather feedback on their solutions through iterative internal experimentation. APs use generative artificial intelligence to enable dynamic multi-modal interaction and utilize user personas to generate contextual feedback. We aim to conduct a series of studies to further validate APs by applying the design science approach as guidance. We plan to develop an initial prototype of APs and conduct studies in a more controlled setting using open-source or completed projects to validate APs. Later, we will transition to ongoing projects in various types and domains. 

The rapid advancement of technology has significantly transformed daily human life, particularly through the widespread use of smartphones to simplify various tasks. In response to this technological shift, Kost XYZ, a women’s boarding house located in Central Jakarta, aims to enhance its operational efficiency. The boarding house faces challenges such as varying room facilities, inconsistent rental rates, non-permanent tenants, and manual record-keeping, all of which frequently result in delayed payments. To address these issues, this study proposes the development of an Android-based financial management information system using the Rapid Application Development (RAD) methodology. The system was built using Kotlin programming language via Android Studio and integrated with Firebase for data storage. Its design provides separate interfaces for owners and tenants: the owner system focuses on boarding house operations, while the tenant system facilitates payment management. Usability testing was conducted using five metrics—learnability, efficiency, memorability, error rate, and satisfaction—with direct user involvement.

Software startup is a unique type of company with unique characteristics. On the one hand, they must offer innovative products appealing to customers to generate revenue and survive, but on the other hand, they are limited in resources, time, and experience. During the new product development, it is important to experiment with their original ideas. However, doing a meaningful experiment requires resources and challenges. A study on failed software startups shows that, despite its importance, many software startups skipped or did not experiment with their ideas. The study identifies 25 inhibitors spread in five experimentation stages. In the last few years, Large Language Models (LLMs) have become a popular technology. The advancement of LLM has made it adopted into many parts of the software development cycle. Studies show that LLM also has been used to generate new innovative product ideas and to manage innovation. However, there is no investigation into the possibility of utilizing the power of LLM to help software startups do experimentation. Interactions to an LLM are done through prompts. During the interaction or session, a user will send one or more prompts in a zero-, one-, or few-shots to an LLM agent. Unfortunately, learning and using prompts effectively requires time and resources, things that software startups are scarce with. In this project, we aim to help improve the experimentation process and address the inhibitors by leveraging the power of LLMs. There are five initial research questions and studies planned in the project. In the first step, we will investigate current experimentation practices, challenges, inhibitors, and the strategies used to circumvent them. Secondly, we will investigate how AI has been used in today's experimentation. Then, we will investigate the set of measurements available to measure the success of an experiment. The next step is to investigate how to support experimentation using LLMs followed by a validation sequence. The first form of support is a prompt guidebook to help software startups use an LLM agent to help their experimentation. The second form is an LLM-based assistant tailored specifically to guide the experimentation process. © 2006 Gesellschaft für Informatik, Bonn.

Experimentation has been considered critical for successful software product and business development, including in video game startups. Video game startups need “wow” qualities that distinguish them from the competition. Thus, they need to continuously experiment to find these qualities before running out of time and resources. In this study, we aimed to explore how these companies perform experimentation. We interviewed four co-founders of video game startups. Our findings identify six practices, or scenarios, through which video game startups conduct experiments and challenges associated with these. The initial results could inform these startups about the possibilities and challenges and guide future research. © The Author(s) 2024.

[Context]: The COVID-19 pandemic has disrupted the global market and workplace landscape. As a response, hybrid work situations have become popular in the software business sector. This way of working has an impact on software companies.

[Objective]: This study investigates software companies' responses to hybrid working. [Method]: We conducted a large-scale survey to achieve our objective. Our results are based on a qualitative analysis of 124 valid responses.

[Results]: The main result of our study is a taxonomy of software companies' impacts on hybrid working at individual, team and organisation levels. We found higher positive responses at individual and organisational levels than negative responses. At the team level, both positive and negative impacts obtained a uniform number of responses.

[Conclusion]: The results indicate that hybrid working became credible with the wave of COVID-19, with 83 positive responses outweighing the 41 negative responses. Software company respondents witnessed better work-life balance, productivity, and efficiency in hybrid working.

Context: Generative AI (GenAI) brings new opportunities to the software industry and the digital economy in a broader context.

Objective: This study aimed to explore and capture the practitioners' perception of GenAI adoption in the fast-paced software industry in the context of developing countries. Method: We conducted online focus group discussions with 18 practitioners from various roles to collect qualitative data. The practitioners have an average of 7.8 years of working experience and have used GenAI for over a year. We employed thematic analysis and the Human-AI Collaboration and Adaptation Framework (HACAF) to identify the influencing factors of GenAI adoption, such as awareness, use cases, and challenges.

Results: The adoption of GenAI technology is evident from practitioners. We identified 22 practical use cases, three of which were novel, i.e., contextualizing solutions, assisting the internal audit process, and benchmarking the internal software development process. We also discovered seven key challenges associated with the GenAI adoption, two of which were novel, namely, no matching use cases and unforeseen benefits. These challenges slow GenAI adoption and potentially hinder developing countries from entering a high-skill industry.

Conclusion: While the adoption of GenAI technology is promising, industry-academia collaboration is needed to find solutions and strategies to address the challenges and maximize its potential benefits.

Context:

The COVID-19 pandemic has had a disruptive impact on how people work and collaborate across all global economic sectors, including software business. While remote working is not new for software engineers, forced WFH situations come with both limitations and opportunities. As the ‘new normal’ for working might be based on the current state of Work-from-home (WFH), it is useful to understand what has happened and learn from that.

Objective:

This study aims to gain insights into how their WFH arrangement impacts project management and software engineering. We are also interested in exploring these impacts in different contexts, such as startups and established companies.

Method:

We conducted a global-scale, cross-sectional survey during the spring and summer 2021. Our results are based on quantitative and qualitative analysis of 297 valid responses.

Results:

We characterize the profile of WFH in both spatial and temporal aspects, together with a set of common collaborative tools and coordination and control mechanisms. We revealed some areas of project management that are relatively more challenging during WFH situations, such as coordination, communication and project planning. We also revealed a mixed picture of the perceived impact of WFH on different software engineering activities.

Conclusion:

WFH is a situational phenomenon which can have both negative and positive impact on software teams. For practitioners, we suggest a unified approach to consider the context of WFH, collaborative tools, associated coordination and control approaches and a process that resolve those aspects that are sensitive to physical interaction.

[Context] Systematic Literature Review (SLR) has been a major type of study published in Software Engineering (SE) venues for about two decades. However, there is a lack of understanding of whether an SLR is really needed in comparison to a more conventional literature review. Very often, SE researchers embark on an SLR with such doubts. We aspire to provide more understanding of when an SLR in SE should be conducted. [Objective] The first step of our investigation was focused on the dataset, i.e., the reviewed papers, in an SLR, which indicates the development of a research topic or area. The objective of this step is to provide a better understanding of the characteristics of the datasets of SLRs in SE. [Method] A research synthesis was conducted on a sample of 170 SLRs published in top-tier SE journals. We extracted and analysed the quantitative attributes of the datasets of these SLRs. [Results] The findings show that the median size of the datasets in our sample is 57 reviewed papers, and the median review period covered is 14 years. The number of reviewed papers and review period have a very weak and non-significant positive correlation. [Conclusions] The results of our study can be used by SE researchers as an indicator or benchmark to understand whether an SLR is conducted at a good time. © 2023 IEEE.

Software development companies commonly use Global Software Development (GSD) in their industry. A competent Scrum team supports the success of the GSD project. This research aims to identify the game components in the form of types, elements, and technologies that contribute to building experience for the Scrum team in a GSD environment. We used a Systematic Literature Review (SLR) with the Kitchenham and snowball method. From the 12 papers, we concluded that none of the games specifically focused on the competence of the Scrum team at GSD. The SLR results also identify ten game elements and technologies as a reference for game development to meet the challenges in GSD. The conclusion is that there are still great opportunities to develop a game that can improve the competence of the GSD Scrum team. Game development should refer to elements and identified technologies. © 2022 IEEE.