Crowdsourced Requirements Engineering Challenges and Solutions:A Software Industry Perspective

Software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Because of its dynamic nature,SW CS has been progressively accepted and adopted in the software industry.However,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and explained.If the requirements are not clear to the development team,it has a significant effect on the quality of the software product.This study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)process.Moreover,solutions to overcome these challenges are also identified.Qualitative data analysis is performed on the interview data collected from software industry professionals.Consequently,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven categories.This study is beneficial for academicians,researchers and practitioners by providing detailed SW–CS based RE challenges and subsequent solutions that could eventually guide them to understand and effectively implement RE in SW CS.

Harnessing Crowdsourced Data and Prevalent Technologies for Atmospheric Research

The knowledge garnered in environmental science takes a crucial part in informing decision-making in various fields,including agriculture, transportation, energy, public health and safety, and more. Understanding the basic processes in each of these fields relies greatly on progress being made in conceptual, observational and technological approaches. However,existing instruments for environmental observations are often limited as a result of technical and practical constraints. Current technologies, including remote sensing systems and ground-level measuring means, may suffer from obstacles such as low spatial representativity or a lack of precision when measuring near ground-level. These constraints often limit the ability to carry out extensive meteorological observations and, as a result, the capacity to deepen the existing understanding of atmospheric phenomena and processes. Multi-system informatics and sensing technology have become increasingly distributed as they are embedded into our environment. As they become more widely deployed, these technologies create unprecedented data streams with extraordinary levels of coverage and immediacy, providing a growing opportunity to complement traditional observation techniques using the large volumes of data created. Commercial microwave links that comprise the data transfer infrastructure of cellular communication networks are an example of these types of systems. This viewpoint letter briefly reviews various works on the subject and presents aspects concerning the added value that may be obtained as a result of the integration of these new means, which are becoming available for the first time in this era, for studying and monitoring atmospheric phenomena.

An effective indoor radio map construction scheme based on crowdsourced samples

The crowdsourcing-based WLAN indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps.Aiming at the problem of the inaccurate location annotation of the crowdsourced samples,the existing invalid access points(APs)in collected samples,and the uneven sample distribution,as well as the diverse terminal devices,which will result in the construction of the wrong radio map,an effective WLAN indoor radio map construction scheme(WRMCS)is proposed based on crowdsourced samples.The WRMCS consists of 4 main modules:outlier detection,key AP selection,fingerprint interpolation,and terminal device calibration.Moreover,an online localization algorithm is put forward to estimate the position of the online test fingerprint.The simulation results show that the proposed scheme can achieve higher localization accuracy than the peer schemes,and possesses good effectiveness and robustness at the same time.

Crowdsourced Sampling of a Composite Random Variable: Analysis, Simulation, and Experimental Test

A composite random variable is a product (or sum of products) of statistically distributed quantities. Such a variable can represent the solution to a multi-factor quantitative problem submitted to a large, diverse, independent, anonymous group of non-expert respondents (the “crowd”). The objective of this research is to examine the statistical distribution of solutions from a large crowd to a quantitative problem involving image analysis and object counting. Theoretical analysis by the author, covering a range of conditions and types of factor variables, predicts that composite random variables are distributed log-normally to an excellent approximation. If the factors in a problem are themselves distributed log-normally, then their product is rigorously log-normal. A crowdsourcing experiment devised by the author and implemented with the assistance of a BBC (British Broadcasting Corporation) television show, yielded a sample of approximately 2000 responses consistent with a log-normal distribution. The sample mean was within ~12% of the true count. However, a Monte Carlo simulation (MCS) of the experiment, employing either normal or log-normal random variables as factors to model the processes by which a crowd of 1 million might arrive at their estimates, resulted in a visually perfect log-normal distribution with a mean response within ~5% of the true count. The results of this research suggest that a well-modeled MCS, by simulating a sample of responses from a large, rational, and incentivized crowd, can provide a more accurate solution to a quantitative problem than might be attainable by direct sampling of a smaller crowd or an uninformed crowd, irrespective of size, that guesses randomly.

An accurate indoor map matching algorithm based on activity detection and crowdsourced Wi-Fi

Map matching has been widely investigated in indoor pedestrian navigation to improve positioning accuracy and robustness.This paper proposes an accurate map matching algorithm based on activity detection and crowdsourced Wi-Fi(AiFiMatch).Firstly, by taking indoor road segments between activity-related locations as nodes, and the activity type from one road segment to another as directed edge, the indoor floor plan is abstracted as a directed graph. Secondly, the smartphone’s motion sensors are utilized to detect different activities based on a decision tree and then the pedestrian’s walking trajectory is divided into subtrajectory sequence according to location-related activities. Finally, the sub-trajectory sequence is matched to the directed graph of indoor floor plan to position the pedestrian by using a Hidden Markov Model(HMM). Simultaneously, Wi-Fi fingerprints are bound to road segments based on timestamp. Through crowdsourcing, a radio map of indoor road segments is constructed. The radio map in turn inversely promotes the HMM based map matching algorithm. AiFiMatch is evaluated by the experiments using smartphones in a teaching building. Experimental results show that the pedestrian can be accurately tracked even without knowing the starting position and AiFiMatch is robust to a certain degree of step length and heading direction errors.

Automatic test report augmentation to assist crowdsourced testing

In crowdsourced mobile application testing, workers are often inexperienced in and unfamiliar with software testing. Meanwhile, workers edit test reports in descriptive natural language on mobile devices. Thus, these test reports generally lack important details and challenge developers in understanding the bugs. To improve the quality of inspected test reports, we issue a new problem of test report augmentation by leveraging the additional useful information contained in duplicate test reports. In this paper, we propose a new framework named test report augmentation framework (TRAF) towards resolving the problem. First, natural language processing (NLP) techniques are adopted to preprocess the crowdsourced test reports. Then, three strategies are proposed to augment the environments, inputs, and descriptions of the inspected test reports, respectively. Finally, we visualize the augmented test reports to help developers distinguish the added information. To evaluate TRAF, we conduct experiments over five industrial datasets with 757 crowdsourced test reports. Experimental results show that TRAF can recommend relevant inputs to augment the inspected test reports with 98.49% in terms of NDCG and 88.65% in terms of precision on average, and identify valuable sentences from the descriptions of duplicates to augment the inspected test reports with 83.58% in terms of precision, 77.76% in terms of recall, and 78.72% in terms of F-measure on average. Meanwhile, empirical evaluation also demonstrates that augmented test reports can help developers understand and fix bugs better.

An Integrated Incentive Framework for Mobile Crowdsourced Sensing

Currently, mobile devices （e.g., smartphones） are equipped with multiple wireless interfaces and rich builtin functional sensors that possess powerful computation and communication capabilities, and enable numerous Mobile Crowdsourced Sensing （MCS） applications. Generally, an MCS system is composed of three components： a publisher of sensing tasks, crowd participants who complete the crowdsourced tasks for some kinds of rewards, and the crowdsourcing platform that facilitates the interaction between publishers and crowd participants. Incentives are a fundamental issue in MCS. This paper proposes an integrated incentive framework for MCS, which appropriately utilizes three widely used incentive methods： reverse auction, gamification, and reputation updating. Firstly, a reverse-auction-based two-round participant selection mechanism is proposed to incentivize crowds to actively participate and provide high-quality sensing data. Secondly, in order to avoid untruthful publisher feedback about sensing-data quality, a gamification-based verification mechanism is designed to evaluate the truthfulness of the publisher＇s feedback. Finally, the platform updates the reputation of both participants and publishers based on their corresponding behaviors. This integrated incentive mechanism can motivate participants to provide high-quality sensed contents, stimulate publishers to give truthful feedback, and make the platform profitable.

Crowdsourced Road Semantics Mapping Based on Pixel-Wise Confidence Level

High-definition map has become a vital cornerstone in the navigation of autonomous vehicles in complex traffic scenarios.Thus,the construction of high-definition maps has become crucial.Traditional methods relying on expensive mapping vehicles equipped with high-end sensor equipment are not suitable for mass map construction because of the limitation imposed by its high cost.Hence,this paper proposes a new method to create a high-definition road semantics map using multi-vehicle sensor data.The proposed method implements crowdsourced point-based visual SLAM to align and combine the local maps derived by multiple vehicles.This allows users to modify the extraction process by using a more sophisticated neural network,thus achieving a more accurate detection result when compared with traditional binarization method.The resulting map consists of road marking points suitable for autonomous vehicle navigation and path-planning tasks.Finally,the method is evaluated on the real-world KAIST urban dataset and Shougang dataset to demonstrate the level of detail and accuracy of the proposed map with 0.369 m in mapping errors in ideal condition.

PARE:Privacy-Preserving Data Reliability Evaluation for Spatial Crowdsourcing in Internet of Things

The proliferation of intelligent,connected Internet of Things(IoT)devices facilitates data collection.However,task workers may be reluctant to participate in data collection due to privacy concerns,and task requesters may be concerned about the validity of the collected data.Hence,it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing(SC)data collection tasks with IoT.To this end,this paper proposes a privacy-preserving data reliability evaluation for SC in IoT,named PARE.First,we design a data uploading format using blockchain and Paillier homomorphic cryptosystem,providing unchangeable and traceable data while overcoming privacy concerns.Secondly,based on the uploaded data,we propose a method to determine the approximate correct value region without knowing the exact value.Finally,we offer a data filtering mechanism based on the Paillier cryptosystem using this value region.The evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection.

Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation.While traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and efficiency.To address these issues,this study proposes a novel two-part invalid detection task allocation framework.In the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data.Compared to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public dataset.In the second step of the framework,task allocation modeling is performed using a twopart graph matching method.This phase introduces a P-queue KM algorithm that implements a more efficient optimization strategy.The allocation efficiency is improved by approximately 23.83%compared to the baseline method.Empirical results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.

Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models.Besides collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and Uber.Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications.Previous research conducted task assignments via traditional matching approaches or using simple network models.However,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in tasks.Therefore,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal allocation.Specifically,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task assignments.Extensive experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.

VGI and crowdsourced data credibility analysis using spam email detection techniques

Volunteered geographic information(VGI)can be considered a subset of crowdsourced data(CSD)and its popularity has recently increased in a number of application areas.Disaster management is one of its key application areas in which the benefits of VGI and CSD are potentially very high.However,quality issues such as credibility,reliability and relevance are limiting many of the advantages of utilising CSD.Credibility issues arise as CSD come from a variety of heterogeneous sources including both professionals and untrained citizens.VGI and CSD are also highly unstructured and the quality and metadata are often undocumented.In the 2011 Australian floods,the general public and disaster management administrators used the Ushahidi Crowd-mapping platform to extensively communicate flood-related information including hazards,evacuations,emergency services,road closures and property damage.This study assessed the credibility of the Australian Broadcasting Corporation’s Ushahidi CrowdMap dataset using a Naïve Bayesian network approach based on models commonly used in spam email detection systems.The results of the study reveal that the spam email detection approach is potentially useful for CSD credibility detection with an accuracy of over 90%using a forced classification methodology.

Development of a Low-Cost Prototype System for Pipeline Operational and Vandalism Spillage Detection and Validation Framework

Crude oil spillage is a major challenge in Nigeria. It affects the environment, health, life, and livelihood of residents of the Niger Delta region, where oil is explored, processed, and transported via a network of pipelines. Oil spillage is primarily caused by vandalization/sabotage and operational issues such as corrosion, equipment failure, operation, and maintenance errors. Thus, prompt response is required to mitigate the impact of oil spills. In this study, we deployed low-cost Arduino systems, including sensors (vibration and flow), modules (GPS and Wifi) and an IoT platform (ThingSpeak) to detect spillage caused by vandalism and operational inefficiencies proactively. The results demonstrate that low-cost sensors can detect changes in the flow volume between the inflow and outflow attributable to spillage, and vibration shocks caused by vandalism can be detected and linked to the cause of the spillage and communicated in real time to inform response action. Moreover, we proposed a framework for field validation utilizing KoboToolBox (a crowdsourcing/citizen science platform). The prototype system designed and programmed showed promising results, as it could detect spillage for vandalism and operational scenarios in real-time, quantify the volume of spillage, and identify the location and time of spillage occurrence;indicators relevant for response planning to minimize the impact of oil spillage. A video demonstration of the prototype system developed is accessible via: https://youtu.be/wKa9MZvYf1w. .

Optimizing Spatial Crowdsourcing:A Quality-Aware Task Assignment Approach for Mobile Communication

The widespread use of advanced electronic devices has led to the emergence of spatial crowdsourcing,a method that taps into collective efforts to perform real-world tasks like environmental monitoring and traffic surveillance.Our research focuses on a specific type of spatial crowdsourcing that involves ongoing,collaborative efforts for continuous spatial data acquisition.However,due to limited budgets and workforce availability,the collected data often lacks completeness,posing a data deficiency problem.To address this,we propose a reciprocal framework to optimize task assignments by leveraging the mutual benefits of spatiotemporal subtask execution.We introduce an entropy-based quality metric to capture the combined effects of incomplete data acquisition and interpolation imprecision.Building on this,we explore a quality-aware task assignment method,corresponding to spatiotemporal assignment strategies.Since the assignment problem is NP-hard,we develop a polynomial-time algorithm with the guaranteed approximation ratio.Novel indexing and pruning techniques are proposed to further enhance performance.Extensive experiments conducted on datasets validate the effectiveness of our methods.

Impact of Crowdsourcee’s Vertical Fairness Concern on the Crowdsourcing Knowledge Sharing Behavior and Its Incentive Mechanism

This paper examines in detail the impact of the crowdsourcee’s vertical fairness concern on the knowledge sharing incentive mechanism in crowdsourcing communities.The conditions for the establishment of the incentive mechanism are analyzed and the impact of fairness concern sensitivity on expected economic revenues of both sides as well as the crowdsourcing project performance is studied by game theory and computer simulation.The results show that the knowledge sharing incentive mechanism can only be established if the ratio between the performance improvement rate and the private cost reduction rate caused by shared knowledge is within a certain range.The degree of the optimal linear incentives,the private solution efforts,and the improvement of knowledge sharing level are positively correlated with the sensitivity of vertical fairness concern.In the non-incentive mode,the ratio between the performance conversion rate of private solution effort and the performance conversion rate of knowledge sharing effort plays an important role in moderating a crowdsourcing project’s performance.The authors find that the number of participants is either conducive or nonconducive to the improvement of performance.The implementation of knowledge sharing incentive can achieve a win-win situation for both the crowdsourcer and the crowdsourcee.

Spotlight: Hot Target Discovery and Localization with Crowdsourced Photos

Camera-equipped mobile devices are encouraging people to take more photos and the development and growth of social networks is making it increasingly popular to share photos online. When objects appear in overlapping Fields Of View(FOV), this means that they are drawing much attention and thus indicates their popularity. Successfully discovering and locating these objects can be very useful for many applications, such as criminal investigations, event summaries, and crowdsourcing-based Geographical Information Systems(GIS).Existing methods require either prior knowledge of the environment or intentional photographing. In this paper, we propose a seamless approach called 'Spotlight', which performs passive localization using crowdsourced photos.Using a graph-based model, we combine object images across multiple camera views. Within each set of combined object images, a photographing map is built on which object localization is performed using plane geometry. We evaluate the system’s localization accuracy using photos taken in various scenarios, with the results showing our approach to be effective for passive object localization and to achieve a high level of accuracy.

Improving the Quality of Crowdsourced Image Labeling via LabelSimilarity

Crowdsourcing is an effective method to obtain large databases of manually-labeled images, which is especially important for image understanding with supervised machine learning algorithms. However, for several kinds of tasks regarding image labeling, e.g., dog breed recognition, it is hard to achieve high-quality results. Therefore, further optimizing crowdsourcing workflow mainly involves task allocation and result inference. For task allocation, we design a two-round crowdsourcing framework, which contains a smart decision mechanism based on information entropy to determine whether to perform the second round task allocation. Regarding result inference, after quantifying the similarity of all labels, two graphical models are proposed to describe the labeling process and corresponding inference algorithms are designed to further improve the result quality of image labeling. Extensive experiments on real-world tasks in Crowdflower and synthesis datasets were conducted. The experimental results demonstrate the superiority of these methods in comparison with state-of-the-art methods.

COSSETS＋： Crowdsourced Missing Value Imputation Optimized byKnowledge Base

Missing value imputation with crowdsourcing is a novel method in data cleaning to capture missing values that could hardly be filled with automatic approaches. However, the time cost and overhead in crowdsourcing are high. Therefore, we have to reduce cost and guarantee the accuracy of crowdsourced imputation. To achieve the optimization goal, we present COSSET+, a crowdsourced framework optimized by knowledge base. We combine the advantages of both knowledge-based filter and crowdsourcing platform to capture missing values. Since the amount of crowd values will affect the cost of COSSET+, we aim to select partial missing values to be crowdsourced. We prove that the crowd value selection problem is an NP-hard problem and develop an approximation algorithm for this problem. Extensive experimental results demonstrate the efficiency and effectiveness of the proposed approaches.

A crowd-sourced genomic project to assess hybrid content in a rare avian vagrant(Azure Tit Cyanistes cyanus(Pallas,1770))

The aim of this study was to correlate plumage variation with the amount of genomic hybrid content in hybrids between Azure Tits Cyanistes cyanus(Pallas,1770)and European Blue Tit Cyanistes caeruleus(Linnaeus,1758),by re-sequencing the genomes of museum specimens of non-hybrids and presumed hybrids with varying plumages.The project was funded by crowdsourcing and initiated when two presumed Azure Tits,observed by hundreds of Swedish birdwatchers,were rejected as hybrids based on minor plumage deviations assumed to indicate hybrid contents from the European Blue Tit.The results confirm that hybrids with intermediate plumages,so called Pleske’s Tits,are first generation hybrids(F1 hybrids).Individuals,whose plumages are similar to Azure Tits,but assessed as hybrids based on minor plumage deviations,are all backcrosses but vary in their degree of hybrid content.However,some individuals morphologically recognized as pure Azure Tits expressed similar degrees of hybrid content.The results indicate that:(1)hybrid content may be widespread in Azure Tits in the western part of its habitat distribution;(2)plumage deviation in backcrosses is not linearly correlated with the genetic degree of hybrid origin;and(3)all Azure Tits observed in Europe outside its natural distribution may have some degree of hybrid origin.We therefore suggest that it is very difficult to phenotypically single out hybrids beyond first generation backcrosses.We argue that decreased sequencing costs and improved analytical tools open the doors for museomic crowd-sourced projects that may not address outstanding biological questions but have a major interest for lay citizens such as birdwatchers.

Acoustic contamination assessment during the transition between the COVID-19 restrictions and reactivation:A exploratory analysis in Guayaquil

Noise pollution is becoming a critical health risk for city life.In 2020,the COVID-19 pandemic forced many cities to implement several mobility restrictions.These restrictions changed human activity patterns and decreased the noise levels and noise pollution that often affect urban settings.As the number of infections decreased,so did the outdoor activities,influencing the population’s perception of noise.This paper aims to evaluate the changes in noise levels associated with mobility restrictions between 2020 and 2021 in Guayaquil,Ecuador.This study used crowdsourcing with the help of smartphones and mobile applications to collect geo-referenced environmental noise data.The data was used to generate noise maps in different time frames.Finally,noise level maps were created using GIS-based tools to identify the urban areas that experienced the highest noise level variation during the study period.The results show that the most significant noise increase occurred at night.Furthermore,when analyzing noise level changes in different urban areas,the western area of Guayaquil was the one that experienced the most significant noise level variation.Findings inform the perception of noise pollution and could potentially serve as a reference for decision-makers during the proposal of public policies that ensure a better quality of life for its citizens.