Nitish Patkar

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I am currently serving as a research associate at FHNW (University of Applied Sciences and Arts, Northwestern Switzerland) within the Institute for Interactive Technologies.

I earned my Ph.D. from the University of Bern. Between 2018 and 2022, I was a member of the Software Composition Group led by Prof. Oscar Nierstrasz. My work primarily focuses on requirements engineering, with a strong interest in UX, digital sustainability, and improving agile way of development.

Discover my doctoral research thesis, titled Supporting Multiple Stakeholders in Agile Development.

Projects I personally find interesting

Challenges and Opportunities for Prompt Management

Accepted at MuC 2024, our paper investigates the usability and user experience of generative AI tools like ChatGPT, with a focus on prompt management. Through an empirical survey of 61 users, we identified challenges in organizing and managing prompts. Our study highlights the need for advanced search functionalities, labeling, and innovative interface designs to enhance efficiency. Importantly, we emphasize the sustainability aspect, as efficient prompt management can significantly reduce the environmental impact of AI technologies. By guiding the development of more user-friendly and sustainable genAI tools, we aim to promote eco-friendly practices in the rapidly growing field of AI.

Data-Driven Persona Creation, Validation, and Evolution

Submitted for SNSF BRIDGE Proof of Concept funding, our paper introduces a data-driven approach to automate the creation, validation, and evolution of personas. By leveraging user feedback and monitoring data, we aim to streamline the persona development process, making it more efficient and accurate. Our research highlights the importance of continuously updating personas to reflect actual user behaviors and needs, thus enhancing the relevance and impact of software design. This approach not only improves user experience but also promotes sustainable practices by reducing redundant development efforts.

Data-Driven Estimation for Feature Implementation

This work-in-progress project aims to develop a data-driven method to accurately estimate the human and time resources required for implementing new features in software projects. By analyzing historical data from feature branches and issues labeled as 'feature,' we seek to answer high-level questions regarding the roles, skills, and project management aspects involved in feature implementation. This approach will provide empirical evidence to improve resource estimation, train AI models for better predictions, and inform other engineering disciplines such as product lines. Ultimately, our goal is to enhance the efficiency and accuracy of feature development in software projects.

Education


Ph.D.
University of Bern, Switzerland
March 2018-March 2022
Research topics: requirements engineering, domain modelling, collaborative development

M.Sc.
University of Paderborn, Germany
March 2014-February 2018
Focus point: Information and database systems

B.E.
University of Mumbai, India
2009-2012

Professional experience


University of Applied Sciences and Arts North-western Switzerland (FHNW), as a Research associate
March 2022-Present

University of Bern, as a Research assistant
February 2018-February 2022

ActiDo GmbH, Paderborn, as a Web developer
2017-2018

University of Paderborn, as a Research assistant
2016-2018

Infosys Ltd., India, as a Systems Engineer
2012-2014

Other


Check out Excerpts Page where I occasionally collect interesting and important lessons that I cherish.