@INPROCEEDINGS{Minani24-CIoT-IoTWearables,
   AUTHOR       = {Jean Baptiste Minani and Yann Ga�l Gu�h�neuc and 
      Naouel Moha and Fatima Sabir and El Fellah, Yahia and Sanam Ahmed},
   BOOKTITLE    = {Proceedings of the 7<sup>th</sup> Conference on Cloud and Internet of Things (CIoT)},
   TITLE        = {An Exploratory Study on Code Quality, Testing, Data 
      Accuracy, and Practical Use Cases of IoT Wearables},
   YEAR         = {2024},
   OPTADDRESS   = {},
   OPTCROSSREF  = {},
   EDITOR       = {Kim Koha Nguyen and Eirini Eleni Tsiropoulou},
   MONTH        = {October},
   NOTE         = {10 pages. Short paper.},
   OPTNUMBER    = {},
   OPTORGANIZATION = {},
   PAGES        = {1&ndash;5},
   PUBLISHER    = {IEEE CS Press},
   OPTSERIES    = {},
   OPTVOLUME    = {},
   KEYWORDS     = {Topic: <b>Evolution patterns</b>, 
      Rubrique : <b>patrons d'�volution</b>, Conference: CIoT},
   URL          = {http://www.ptidej.net/publications/documents/CIoT24b.doc.pdf},
   PDF          = {http://www.ptidej.net/publications/documents/CIoT24b.ppt.pdf},
   ABSTRACT     = {The growth of the Internet of Things (IoT), particularly 
      in wearable devices like Fitbits, has raised challenges related to 
      source code quality, testing, data accuracy, and practical 
      applications. This paper investigates issues in Fitbit apps by (1) 
      analyzing GitHub repositories of Fitbit projects to identify code 
      quality issues, (2) using Large Language Models (LLMs) to automate 
      testing, (3) comparing data variations across different Fitbit 
      models, and (4) experimenting with real-world use cases for Fitbit 
      devices. Our analysis of 16 GitHub repositories revealed code quality 
      issues in Fitbit apps, highlighting the need for better practices. 
      Using LLMs like ChatGPT-4, we generated unit tests with 100% 
      coverage. Data comparisons across Fitbit Versa models showed 
      consistent accuracy. Finally, we showed the potential of wearable 
      devices in the real-world with two practical use cases: health 
      monitoring with robotic assistance and location-based tracking. These 
      findings open new avenues for research in wearables.}
}

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G.","Moha, N.","Sabir, F.","El Fellah, Y.","Ahmed, S."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Jean","Baptiste"],"propositions":[],"lastnames":["Minani"],"suffixes":[]},{"firstnames":["Yann","Ga�l"],"propositions":[],"lastnames":["Gu�h�neuc"],"suffixes":[]},{"firstnames":["Naouel"],"propositions":[],"lastnames":["Moha"],"suffixes":[]},{"firstnames":["Fatima"],"propositions":[],"lastnames":["Sabir"],"suffixes":[]},{"propositions":[],"lastnames":["El","Fellah"],"firstnames":["Yahia"],"suffixes":[]},{"firstnames":["Sanam"],"propositions":[],"lastnames":["Ahmed"],"suffixes":[]}],"booktitle":"Proceedings of the 7^th Conference on Cloud and Internet of Things (CIoT)","title":"An Exploratory Study on Code Quality, Testing, Data Accuracy, and Practical Use Cases of IoT Wearables","year":"2024","optaddress":"","optcrossref":"","editor":[{"firstnames":["Kim","Koha"],"propositions":[],"lastnames":["Nguyen"],"suffixes":[]},{"firstnames":["Eirini","Eleni"],"propositions":[],"lastnames":["Tsiropoulou"],"suffixes":[]}],"month":"October","note":"10 pages. Short paper.","optnumber":"","optorganization":"","pages":"1&ndash;5","publisher":"IEEE CS Press","optseries":"","optvolume":"","keywords":"Topic: <b>Evolution patterns</b>, Rubrique : <b>patrons d'�volution</b>, Conference: CIoT","url":"http://www.ptidej.net/publications/documents/CIoT24b.doc.pdf","pdf":"http://www.ptidej.net/publications/documents/CIoT24b.ppt.pdf","abstract":"The growth of the Internet of Things (IoT), particularly in wearable devices like Fitbits, has raised challenges related to source code quality, testing, data accuracy, and practical applications. This paper investigates issues in Fitbit apps by (1) analyzing GitHub repositories of Fitbit projects to identify code quality issues, (2) using Large Language Models (LLMs) to automate testing, (3) comparing data variations across different Fitbit models, and (4) experimenting with real-world use cases for Fitbit devices. Our analysis of 16 GitHub repositories revealed code quality issues in Fitbit apps, highlighting the need for better practices. Using LLMs like ChatGPT-4, we generated unit tests with 100% coverage. Data comparisons across Fitbit Versa models showed consistent accuracy. Finally, we showed the potential of wearable devices in the real-world with two practical use cases: health monitoring with robotic assistance and location-based tracking. These findings open new avenues for research in wearables.","bibtex":"@INPROCEEDINGS{Minani24-CIoT-IoTWearables,\r\n AUTHOR = {Jean Baptiste Minani and Yann Ga�l Gu�h�neuc and \r\n Naouel Moha and Fatima Sabir and El Fellah, Yahia and Sanam Ahmed},\r\n BOOKTITLE = {Proceedings of the 7^th Conference on Cloud and Internet of Things (CIoT)},\r\n TITLE = {An Exploratory Study on Code Quality, Testing, Data \r\n Accuracy, and Practical Use Cases of IoT Wearables},\r\n YEAR = {2024},\r\n OPTADDRESS = {},\r\n OPTCROSSREF = {},\r\n EDITOR = {Kim Koha Nguyen and Eirini Eleni Tsiropoulou},\r\n MONTH = {October},\r\n NOTE = {10 pages. Short paper.},\r\n OPTNUMBER = {},\r\n OPTORGANIZATION = {},\r\n PAGES = {1&ndash;5},\r\n PUBLISHER = {IEEE CS Press},\r\n OPTSERIES = {},\r\n OPTVOLUME = {},\r\n KEYWORDS = {Topic: <b>Evolution patterns</b>, \r\n Rubrique : <b>patrons d'�volution</b>, Conference: CIoT},\r\n URL = {http://www.ptidej.net/publications/documents/CIoT24b.doc.pdf},\r\n PDF = {http://www.ptidej.net/publications/documents/CIoT24b.ppt.pdf},\r\n ABSTRACT = {The growth of the Internet of Things (IoT), particularly \r\n in wearable devices like Fitbits, has raised challenges related to \r\n source code quality, testing, data accuracy, and practical \r\n applications. This paper investigates issues in Fitbit apps by (1) \r\n analyzing GitHub repositories of Fitbit projects to identify code \r\n quality issues, (2) using Large Language Models (LLMs) to automate \r\n testing, (3) comparing data variations across different Fitbit \r\n models, and (4) experimenting with real-world use cases for Fitbit \r\n devices. 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