The term crowdsourcing – a compound contraction of crowd and outsourcing – is a new paradigm for utilizing the power of crowds of people to facilitate large-scale tasks that are costly or time consuming with traditional methods. This paradigm offers mobile application companies the possibility to outsource their testing activities to crowdsourced testers (crowdtesters) who have various testing facilities and environments, as well as different levels of skills and expertise. With this so-called Crowdsourced Mobile Application Testing (CMAT), some of the well-recognized issues in testing mobile applications, such as multitude of mobile devices, fragmentation of device models, variety of OS versions, and omnifariousness of testing scenarios, could be mitigated. However, how effective is CMAT in practice? What are the challenges and issues presented by the process of applying CMAT? How can these issues and challenges be overcome and CMAT be improved? Although CMAT has attracted attention from both academia and industry, these questions have not been addressed or researched in depth based on a large-scale and real-life industrial study. Since June 2015, we have worked with Mooctest, Inc., a CMAT intermediary, on testing five real-life Android applications using their CMAT platform – Kikbug. Throughout the process, we have collected 1013 bug reports from 258 crowdtesters and found 247 bugs in total. This paper will present our industrial study thoroughly and give an insightful analysis to investigate the successes and challenges of applying CMAT.
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Crowdsourced Testing Services for Mobile Apps
Recent publications have pointed out a number of challenges and challenges for when engineers validate mobile apps using a conventional way inside a testing laboratory. Top two issues include: a) higher test costs due to the diversity of mobile devices and platforms, b) difficulty in conducting large-sale user-oriented performance and usability testing. A new testing approach, known as crowdsourced testing, provides a promising way to address these challenges and issues. This paper provides a comprehensive tutorial on crowdsourced test services, and informative concepts, insights, and detailed discussion about common questions raised by engineers and managers. It presents a clear comparative view between mobile crowdsourced testing with traditional lab-based mobile testing. In addition, it also summarizes and compares different major players, their commercial products, and solutions in mobile crowdsourced test services. Furthermore, it examines the major issues, challenges, and needs.
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- Award ID(s):
- 1637371
- NSF-PAR ID:
- 10092502
- Date Published:
- Journal Name:
- 2017 IEEE Symposium on Service-Oriented System Engineering (SOSE)
- Page Range / eLocation ID:
- 75 to 80
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Background Rural and remote communities were especially vulnerable to the COVID-19 pandemic due to the availability and capacity of rural health services. Research has found that key issues surrounded (1) the lack of staff, (2) the need for coordinated health services, and (3) operational and facility issues. Similarly, research also confirms that irrespective of hospital capacity issues existing during crisis, compared to urban communities, rural communities typically face poorer access to health services. Telehealth programs have long held promise for addressing health disparities perpetuated by inadequate health care access. In response to the current COVID-19 pandemic, Adventist Health Saint Helena Hospital, a rural hospital in northern California, urgently worked to expand telehealth services. However, as Adventist Health Saint Helena Hospital is the longest-serving rural hospital in the state of California, administrators were also able to draw on experiences from the pandemic of 1918/1919. Understanding their historically rural and heavily Latino populations, their telehealth approach was coupled with cultural approaches for prioritizing socially responsive and equitable access to health services.
Objective This study aimed to present one rural community’s holistic sociotechnical response to COVID-19 in redesigning their health care delivery approach. Redesign efforts included the expansion of digital health services coupled with county-wide collaborations for nondigital mobile health centers, testing, and vaccination clinics to meet the needs of those with limited digital access and language barriers.
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Results In this paper, we provide a critical review of how a rural hospital adapted its health care approach to incorporate telehealth services and distance services to meet the needs of a diverse population.
Conclusions This paper contributes empirical data on how rural communities can use telehealth technologies and community partnerships for a holistic community approach to meet health needs during a natural disaster.
Conflicts of Interest None declared.
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null (Ed.)The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SOSE.2017.28
