Anh Nguyen Duc

Kien Tran Duc Tuan, Tam Nguyen Trong, Son Nguyen Hoang et al.

Integrated Gradients (IG) is a widely used attribution method in explainable AI, particularly in computer vision applications where reliable feature attribution is essential. A key limitation of IG is its sensitivity to the choice of baseline (reference) images. Multi-baseline extensions such as Expected Gradients (EG) assume uniform weighting over baselines, implicitly treating baseline images as equally informative. In high-dimensional vision models, this assumption often leads to noisy or unstable explanations. This paper proposes Weighted Integrated Gradients (WG), a principled approach that evaluates and weights baselines to enhance attribution reliability. WG introduces an unsupervised criterion for baseline suitability, enabling adaptive selection and weighting of baselines on a per-input basis. The method not only preserves core axiomatic properties of IG but also provides improved theoretical guarantees on the quality of explanation over EG. Experiments on commonly used image datasets and models show that WG consistently outperforms EG, yielding 10 to 35 percent improvements in attribution fidelity. WG further identifies informative baseline subsets, reducing unnecessary variability while maintaining high attribution accuracy. By moving beyond the idea that all baselines matter equally, Weighted Integrated Gradients offers a clearer and more reliable way to explain computer-vision models, improving both understanding and practical usability in explainable AI.

Anh Nguyen Duc, Xiaofang Weng, Pekka Abrahamsson

[Context]Advancement in technologies, popularity of small-batch manufacturing and the recent trend of investing in hardware startups are among the factors leading to the rise of hardware startups nowadays. It is essential for hardware startups to be not only agile to develop their business but also efficient to develop the right products. [Objective] We investigate how hardware startups achieve agility when developing their products in early stages. [Methods] A qualitative research is conducted with data from 20 hardware startups. [Result] Preliminary results show that agile development is known to hardware entrepreneurs, however it is adopted limitedly. We also found tactics in four domains (1) strategy, (2) personnel, (3) artifact and (4) resource that enable hardware startups agile in their early stage business and product development. [Conclusions] Agile methodologies should be adopted with the consideration of specific features of hardware development, such as up-front design and vendor dependencies.

Aparna Vegendla, Anh Nguyen Duc, Shang Gao et al.

Software ecosystems (SECOs) and open innovation processes have been claimed as a way forward for the software industry. A proper understanding of requirements is as important for these IT-systems as for more traditional ones. This paper presents a mapping study on the issues of requirements engineering and quality aspects in SECOs and analyzes emerging ideas. Our findings indicate that among the various phases or subtasks of requirements engineering, most of the SECO specific research has been accomplished on elicitation, analysis, and modeling. On the other hand, requirements selection, prioritization, verification, and traceability has attracted few published studies. Among the various quality attributes, most of the SECOs research has been performed on security, performance and testability. On the other hand, reliability, safety, maintainability, transparency, usability attracted few published studies. The paper provides a review of the academic literature about SECO-related requirements engineering activities, modeling approaches, and quality attributes, positions the source publications in a taxonomy of issues and identifies gaps where there has been little research.

Anh Nguyen Duc, Xiaofeng Wang, Pekka Abrahamsson

It is essential for startups to quickly experiment business ideas by building tangible prototypes and collecting user feedback on them. As prototyping is an inevitable part of learning for early stage software startups, how fast startups can learn depends on how fast they can prototype. Despite of the importance, there is a lack of research about prototyping in software startups. In this study, we aimed at understanding what are factors influencing different types of prototyping activities. We conducted a multiple case study on twenty European software startups. The results are two folds, firstly we propose a prototype-centric learning model in early stage software startups. Secondly, we identify factors occur as barriers but also facilitators for prototyping in early stage software startups. The factors are grouped into (1) artifacts, (2) team competence, (3) collaboration, (4) customer and (5) process dimensions. To speed up a startups progress at the early stage, it is important to incorporate the learning objective into a well-defined collaborative approach of prototyping

Anh Nguyen Duc, Hilde Gudvangen

Recommender systems (RSs) have been popular in variety of application domains due to the increased demand for filtering and sorting items and information. Today, there is a numerous approaches and algorithms of data filtering and recommendations. This works presents a conceptual framework for constructing a mobile RS in hyper-local news domain. The mobile RS is designed to deal with specific requirements of news readers, such as spatial- temporal relevance, recency, real-time update and validated news. The implementation of the RS in a distributed file system is also discussed.

Anh Nguyen Duc, Pekka Abrahamsson

Software startups are becoming increasingly popular in software industry as well as other sectors of economy. Startups that lack necessary competences often seek for external resources from outsourcing partners. Little is known how this outsourcing relationship works and whether it makes sense to outsource the technical competence to an external party. This is among the first investigations on the outsourcing relationships in software startups. By conducting exploratory case studies at six startups, we found a mixed experience with outsourcing. The experimental nature of an early product development makes outsourcing a feasible option, although startups often suffer from its uncertainty and managing commitments from partners. Results further propose that early contract-based activities could be transformed into a long-term partnership by adopting a startup boundary spanner s role, establishing an inter-personal relationship and maintaining a mutual commitment.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen Duc et al.

In the context of software startups, project failure is embraced actively and considered crucial to obtain validated learning that can lead to pivots. A pivot is the strategic change of a business concept, product or the different elements of a business model. A better understanding is needed on different types of pivots and different factors that lead to failures and trigger pivots, for software entrepreneurial teams to make better decisions under chaotic and unpredictable environment. Due to the nascent nature of the topic, the existing research and knowledge on the pivots of software startups are very limited. In this study, we aimed at identifying the major types of pivots that software startups make during their startup processes, and highlighting the factors that fail software projects and trigger pivots. To achieve this, we conducted a case survey study based on the secondary data of the major pivots happened in 49 software startups. 10 pivot types and 14 triggering factors were identified. The findings show that customer need pivot is the most common among all pivot types. Together with customer segment pivot, they are common market related pivots. The major product related pivots are zoom-in and technology pivots. Several new pivot types were identified, including market zoom-in, complete and side project pivots. Our study also demonstrates that negative customer reaction and flawed business model are the most common factors that trigger pivots in software startups. Our study extends the research knowledge on software startup pivot types and pivot triggering factors. Meanwhile it provides practical knowledge to software startups, which they can utilize to guide their effective decisions on pivoting