Search Results (7)

Crowdsourcing software design (CSD) is the completion of specific software-design tasks on behalf of a client by a large, unspecified group of external individuals who have the specialized knowledge required by an open call. Although current CSD platforms have provided features to improve coordination in the CSD process (such as email notifications, chat, and announcements), these features are insufficient to solve the coordination limitations. A lack of appropriate coordination support in CSD activities may cause delays and missed opportunities for participants, and thus the best quality of design contest results may not be guaranteed. This research aims to support the effective management of the CSD process through identifying the key activity dependencies among participants in CSD platforms and designing a set of process models to provide coordination support through managing this activity. In order to do this, a five-stage approach was used: First, the current CSD process was investigated by reviewing 13 CSD platforms. Second, the review resulted in the identification of 17 possible suggestions to improve CSD. These suggestions were evaluated in stage 3 through distributing a survey to 41 participants who had experience in using platforms in the field of CSD. In stage 4, we designed ten process models that could meet the requirements of suggestions, while in stage 5, we evaluated these process models through interviews with domain experts. The result shows that coordination support in the activities of the CSD can make valuable contributions to the development of CSD platforms. Full article

Mobile apps have revolutionized almost every aspect of our daily lives, shaping the way we shop, learn and work. The transformative and unprecedented impact they have made on our lifestyle and the convenience they have offered have increased their adoption in diverse domains. Therefore, it is of paramount importance to hear from the interested audience about their desires and requirements in mobile apps. This has stressed the need to employ crowdsourcing in requirements engineering (RE) activities to harness the scattered talent in the crowd. RE tasks require certain software domain knowledge, hence, selecting a suitable subset of the crowd is crucial to obtain high-quality contributions. For that, we propose a crowd selection approach for crowdsourcing mobile app requirements engineering tasks which leverages the untapped crowd available on the social network Twitter (recently changed to X). This article is an extension of our previous work, where we present the proposed social-network-based crowd selection approach design, continue to work on the remaining component of the approach and evaluate the approach through a controlled experiment. For evaluation, the approach was utilized to select a real crowd that were invited to contribute to crowdsourcing requirements elicitation tasks for a fitness mobile app. The quality of the crowdsourced requirements was assessed by experts and the results have provided encouraging and compelling insights about the effectiveness of the proposed approach. The obtained assessment scores for the five quality factors clarity, creativity, relatedness, feasibility and diversity were respectively 4.36, 4.01, 4.29, 4.45 and 4.43 out of 5. Overall, we believe that the proposed social-network-based crowd selection approach could help in eliciting mobile app requirements and features that could cater to the needs of a large audience. Full article

Crowdsourcing Software Development (CSD) platforms, i.e., TopCoder, function as intermediaries connecting clients with developers. Despite employing systematic methodologies, these platforms frequently encounter high task abandonment rates, with approximately 19% of projects failing to meet satisfactory outcomes. Although existing research has focused on task scheduling, developer recommendations, and reward mechanisms, there has been insufficient attention to the support of platform moderators, or copilots, who are essential to project success. A critical responsibility of copilots is estimating project duration; however, manual predictions often lead to inconsistencies and delays. This paper introduces an innovative machine learning approach designed to automate the prediction of project duration on CSD platforms. Utilizing historical data from TopCoder, the proposed method extracts pertinent project attributes and preprocesses textual data through Natural Language Processing (NLP). Bidirectional Encoder Representations from Transformers (BERT) are employed to convert textual information into vectors, which are then analyzed using various machine learning algorithms. Zero-shot learning algorithms exhibit superior performance, with an average accuracy of 92.76%, precision of 92.76%, recall of 99.33%, and an f-measure of 95.93%. The implementation of the proposed automated duration prediction model is crucial for enhancing the success rate of crowdsourcing projects, optimizing resource allocation, managing budgets effectively, and improving stakeholder satisfaction. Full article

Enhancing the efficiency of windows is important for improving the energy efficiency of buildings. The Korean government has performed numerous building renovation projects to reduce greenhouse gas emissions and mitigate energy poverty. To reduce the costs and manpower requirements of conventional field surveys, this study presents a deep-learning model to examine the insulation performance of windows using photographs taken in low-income housing. A smartphone application using crowdsourcing was developed for data collection. The insulation performance of windows was determined based on U-value, derived considering the frame-material type, number of panes, and area of windows. An image-labeling tool was designed to identify and annotate window components within photographs. Furthermore, software utilizing open-source computer vision was developed to estimate the window area. After training on a dataset with ResNet and EfficientNet, an accuracy of approximately 80% was achieved. Thus, this study introduces a novel workflow to evaluate the insulation performance of windows, which can support the energy-efficient renovation of low-income housing. Full article

Purpose: There are currently limited treatment options for aniridia. In this context, 3D printed iris implants may provide a cost-effective, cosmetically acceptable alternative for patients with aniridia. The purpose of this study was to develop a proof-of-concept workflow for manufacturing 3D printed iris implants using a silicone ink palette that aesthetically matches iris shades, identified in slit lamp images. Methods: Slit lamp iris photos from 11 healthy volunteers (3 green; 4 blue; 4 brown) were processed using k-means binning analyses to identify two or three prominent colors each. Candidate silicone inks were created by precisely combining pigments. A crowdsourcing survey software was used to determine color matches between the silicone ink swatches and three prominent iris color swatches in 2 qualifying and 11 experimental workflows. Results: In total, 54 candidate silicone inks (20 brown; 16 green; 18 blue) were developed and analyzed. Survey answers from 29 individuals that had passed the qualifying workflow were invited to identify “best matches” between the prominent iris colors and the silicone inks. From this color-match data, brown, blue, and green prototype artificial irises were printed with the silicone ink that aesthetically matched the three prominent colors. The iris was printed using a simplified three-layer five-branch starburst design at scale (12.8 mm base disc, with 3.5 mm pupil). Conclusions: This proof-of-concept workflow produced color-matched silicone prosthetic irises at scale from a panel of silicone inks using prominent iris colors extracted from slit lamp images. Future work will include printing a more intricate iris crypt design and testing for biocompatibility. Full article

Crowdsourced software engineering (CSE) is an emerging area that has been gaining much attention in the last few years. It refers to the use of crowdsourcing techniques in software engineering activities, including requirements engineering, implementation, design, testing, and verification. CSE is an alternative to traditional software engineering and uses an open call to which online developers can respond to and obtain work on various tasks, as opposed to the assigning of tasks to in-house developers. The great benefits of CSE have attracted the attention of many researchers, and many studies have recently been carried out in the field. This research aims to analyze publications on CSE using social network analysis (SNA). A total of 509 CSE publications from six popular databases were analyzed to determine the characteristics of the collaborative networks of co-authorship of the research (i.e., the co-authors, institutions involved in co-authorship, and countries involved in co-authorship) and of the citation networks on which the publications of the studies are listed. The findings help identify CSE research productivity, trends, performances, community structures, and relationships between various collaborative patterns to provide a more complete picture of CSE research. Full article

OpenStreetMap provides a valuable crowd-sourced database of raw geospatial data for constructing models of urban street networks for scientific analysis. This paper reports results from a research project that collected raw street network data from OpenStreetMap using the Python-based OSMnx software for every U.S. city and town, county, urbanized area, census tract, and Zillow-defined neighborhood. It constructed nonplanar directed multigraphs for each and analyzed their structural and morphological characteristics. The resulting data repository contains over 110,000 processed, cleaned street network graphs (which in turn comprise over 55 million nodes and over 137 million edges) at various scales—comprehensively covering the entire U.S.—archived as reusable open-source GraphML files, node/edge lists, and GIS shapefiles that can be immediately loaded and analyzed in standard tools such as ArcGIS, QGIS, NetworkX, graph-tool, igraph, or Gephi. The repository also contains measures of each network’s metric and topological characteristics common in urban design, transportation planning, civil engineering, and network science. No other such dataset exists. These data offer researchers and practitioners a new ability to quickly and easily conduct graph-theoretic circulation network analysis anywhere in the U.S. using standard, free, open-source tools. Full article