User feedback has grown in importance for organizations to improve software products. Prior studies focused primarily on feedback collection and reported a high-level overview of the processes, often overlooking how practitioners reason about, and act upon this feedback through a structured set of activities. In this work, we conducted an exploratory interview study with 40 practitioners from 32 organizations of various sizes and in several domains such as e-commerce, analytics, and gaming. Our findings indicate that organizations leverage many different user feedback sources. Social media emerged as a key category of feedback that is increasingly critical for many organizations. We found that organizations actively engage in a number of non-trivial activities to curate and act on user feedback, depending on its source. We synthesize these activities into a life cycle of managing user feedback. We also report on the best practices for managing user feedback that we distilled from responses of practitioners who felt that their organization effectively understood and addressed their users' feedback. We present actionable empirical results that organizations can leverage to increase their understanding of user perception and behavior for better products thus reducing user attrition.

With the onset of the COVID-19 pandemic and the ensuing shift away from co-located work arrangements towards working from home, practitioners encountered many collabora-tive and coordination challenges. While companies are slowly moving back towards in-person arrangements, many employers have permanently adopted fully remote and hybrid work modes. However, the work modes are more blurred, as hybrid work makes coordinating the requirements engineering practices that rely on rich interactions more challenging. Therefore, gaining more understanding of RE challenges and practices from prac-titioners transitioning to the new modes of work is imperative to identify insights that can be useful for organizations shifting to hybrid and remote work. In this paper, we use a mixed-methods approach to gain insights into remote and hybrid requirements engineering practices and challenges in the industry. Through interviews with 12 industry practitioners and a survey with 49 practitioners, we report on 7 adopted practices and 7 challenges encountered in these work arrangements. We found challenges such as organizing co-located tasks, lack of interpersonal con-nections, keeping everyone in the loop, and engagement barriers, which fall under coordination, communication and collaboration. To offset such challenges, we provide 20 recommendations based on our findings, such as proactive planning and using newer tools that support comprehensive tracking of important knowl-edge for requirements documentation. Our findings suggest that practitioners are facing challenges in remote and hybrid work arrangements, which they are mitigating with various strategies. Nonetheless, there remains a need for further research, as not all challenges are equally addressed across different work contexts.

The increasing importance of videos as a medium for engagement, communication, and content creation makes them critical for organizations to consider for user feedback. However, sifting through vast amounts of video content on social media platforms to extract requirements-relevant feedback is challenging. This study delves into the use of TikTok and YouTube, two widely used social media platforms that focus on video content, in identifying relevant user feedback that may be further refined into requirements using subsequent requirement generation steps. We demonstrate an approach of using videos as a source of user feedback by analyzing audio and visual text, and metadata (i.e., description/title) from 6276 videos of 20 popular products across various industries. We employed state-of-the-art deep learning transformer-based models, and classified 3097 videos consisting of requirements relevant information. We then clustered relevant videos and found multiple requirements relevant feedback themes for each of the 20 products. This feedback can later be refined into requirements artifacts. We found that product ratings (feature, design, performance), bug reports, and usage tutorial are persistent themes from the videos. Video-based social media such as TikTok and YouTube can provide valuable user insights, making them a powerful and novel resource for companies to improve customer-centric development.

Privacy requirements are increasingly growing in importance as new privacy regulations are enacted. To adequately manage privacy requirements, organizations not only need to comply with privacy regulations, but also consider user privacy concerns. In this exploratory study, we used Reddit as a source to understand users’ privacy concerns regarding software applications. We collected 4.5 million posts from Reddit and classified 129075 privacy related posts, which is a non-negligible number of privacy discussions. Next, we clustered these posts and identified 9 main areas of privacy concerns. We use the concept of narratives from economics (i.e., posts that can go viral) to explain the phenomenon of what and when users change in their discussion of privacy. We further found that privacy discussions change over time and privacy regulatory events have a short term impact on such discussions. However, narratives have a notable impact on what and when users discussed about privacy. Considering narratives could guide software organizations in eliciting the relevant privacy concerns before developing them as privacy requirements.

Remote sensing satellite images have not been thoroughly studied in agricultural sector of Bangladesh.This study demonstrates a rice model using the vegetation index (VI) NDVI (Normalized Difference Vegetation Index) for predicting Boro rice areas in the Haor region of Bangladesh. Existing rice models are not compatible for the latest version of MODIS data. A new rice model with a better range of NDVI (Normalized Difference Vegetation Index) threshold has been proposed for MODIS version 6 and 5 data which provides better results of Boro rice area calculation also when evaluated with ground truth area the error rate reduced from 8.85% to 7.58% on version 5 and from 27.70% to 4.98% on version 6 estimation respectively.

This paper demonstrates an approach to develop a prediction based model for forecasting Boro rice areas in the haor region of Bangladesh. Forecasting the rice areas can contribute in creating a centralized monitoring system for planning effi-cient storage and proper utilization methods. This leads to the development of proposing a new vegetation index (VI). The approach considers a new vegetation index combining NDVI (Normalized Difference Vegetation Index), EVI2 (Enhanced Vegetation Index 2) and OSAVI (Optimized Soil-Adjusted Vegetation Index) for latest version MODIS (version-6) data. The method will forecast total Boro rice areas at the beginning of the Boro season (Dec-Jan) which is more than 3 months earlier from harvesting time without using any ground truth data. 3 Dimensional plotting method and k-Nearest Neighbor classifier have been used on only sowing period (Dec-Jan) data to predict Boro rice pixels. Our new VI has achieved an accuracy of 72%, recall 0.7020, precision 0.4183 and F1score 0.5175.