@ARTICLE{DosSantos24-FiRaA-AAT4IRS,
   AUTHOR       = {Dos Santos, Marcela G. and F�bio Petrillo and 
      Sylvain Hall� and Yann-Ga�l Gu�h�neuc},
   JOURNAL      = {Frontiers in Robotics and AI (FRAI)},
   TITLE        = {AAT4IRS: Automated Acceptance Testing for Industrial 
      Robotic Systems},
   YEAR         = {2024},
   MONTH        = {October},
   OPTNOTE      = {},
   OPTNUMBER    = {},
   OPTPAGES     = {},
   VOLUME       = {11},
   EDITOR       = {Kostas J. Kyriakopoulos},
   KEYWORDS     = {Topic: <b>ESE for the IoT</b>, 
      Rubrique : <b>GL empirique pour l'IdO</b>, Journal: <b>FRAI</b>},
   PUBLISHER    = {Frontiers},
   URL          = {http://www.ptidej.net/publications/documents/FRAI24.doc.pdf},
   ABSTRACT     = {Industrial robotic systems (IRS) consist of industrial 
      robots that automate industrial processes. They accurately perform 
      repetitive tasks, replacing or assisting with dangerous jobs like 
      assembly in the automotive and chemical industries. Failures in these 
      systems can be catastrophic, so it is important to ensure their 
      quality and safety before using them. One way to do this is by 
      applying a software testing process to find faults before they become 
      failures. However, software testing in industrial robotic systems has 
      some challenges. These include differences in perspectives on 
      software testing from people with diverse backgrounds, coordinating 
      and collaborating with diverse teams, and performing software testing 
      within the complex integration inherent in industrial environments. 
      In traditional systems, a well-known development process uses simple, 
      structured sentences in English to facilitate communication between 
      project team members and business stakeholders. This process is 
      called behavior-driven development (BDD), and one of its pillars is 
      the use of templates to write user stories, scenarios, and automated 
      acceptance tests. We propose a software testing (ST) approach called 
      automated acceptance testing for industrial robotic systems (AAT4IRS) 
      that uses natural language to write the features and scenarios to be 
      tested. We evaluated our ST approach through a proof-of-concept, 
      performing a pick-and-place process and applying mutation testing to 
      measure its effectiveness. The results show that the test suites 
      implemented using AAT4IRS were highly effective, with 79% of the 
      generated mutants detected, thus instilling confidence in the 
      robustness of our approach.}
}

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G.","Petrillo, F.","Hall�, S.","Gu�h�neuc, Y."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Dos","Santos"],"firstnames":["Marcela","G."],"suffixes":[]},{"firstnames":["F�bio"],"propositions":[],"lastnames":["Petrillo"],"suffixes":[]},{"firstnames":["Sylvain"],"propositions":[],"lastnames":["Hall�"],"suffixes":[]},{"firstnames":["Yann-Ga�l"],"propositions":[],"lastnames":["Gu�h�neuc"],"suffixes":[]}],"journal":"Frontiers in Robotics and AI (FRAI)","title":"AAT4IRS: Automated Acceptance Testing for Industrial Robotic Systems","year":"2024","month":"October","optnote":"","optnumber":"","optpages":"","volume":"11","editor":[{"firstnames":["Kostas","J."],"propositions":[],"lastnames":["Kyriakopoulos"],"suffixes":[]}],"keywords":"Topic: <b>ESE for the IoT</b>, Rubrique : <b>GL empirique pour l'IdO</b>, Journal: <b>FRAI</b>","publisher":"Frontiers","url":"http://www.ptidej.net/publications/documents/FRAI24.doc.pdf","abstract":"Industrial robotic systems (IRS) consist of industrial robots that automate industrial processes. They accurately perform repetitive tasks, replacing or assisting with dangerous jobs like assembly in the automotive and chemical industries. Failures in these systems can be catastrophic, so it is important to ensure their quality and safety before using them. One way to do this is by applying a software testing process to find faults before they become failures. However, software testing in industrial robotic systems has some challenges. These include differences in perspectives on software testing from people with diverse backgrounds, coordinating and collaborating with diverse teams, and performing software testing within the complex integration inherent in industrial environments. In traditional systems, a well-known development process uses simple, structured sentences in English to facilitate communication between project team members and business stakeholders. This process is called behavior-driven development (BDD), and one of its pillars is the use of templates to write user stories, scenarios, and automated acceptance tests. We propose a software testing (ST) approach called automated acceptance testing for industrial robotic systems (AAT4IRS) that uses natural language to write the features and scenarios to be tested. We evaluated our ST approach through a proof-of-concept, performing a pick-and-place process and applying mutation testing to measure its effectiveness. The results show that the test suites implemented using AAT4IRS were highly effective, with 79% of the generated mutants detected, thus instilling confidence in the robustness of our approach.","bibtex":"@ARTICLE{DosSantos24-FiRaA-AAT4IRS,\r\n AUTHOR = {Dos Santos, Marcela G. and F�bio Petrillo and \r\n Sylvain Hall� and Yann-Ga�l Gu�h�neuc},\r\n JOURNAL = {Frontiers in Robotics and AI (FRAI)},\r\n TITLE = {AAT4IRS: Automated Acceptance Testing for Industrial \r\n Robotic Systems},\r\n YEAR = {2024},\r\n MONTH = {October},\r\n OPTNOTE = {},\r\n OPTNUMBER = {},\r\n OPTPAGES = {},\r\n VOLUME = {11},\r\n EDITOR = {Kostas J. Kyriakopoulos},\r\n KEYWORDS = {Topic: <b>ESE for the IoT</b>, \r\n Rubrique : <b>GL empirique pour l'IdO</b>, Journal: <b>FRAI</b>},\r\n PUBLISHER = {Frontiers},\r\n URL = {http://www.ptidej.net/publications/documents/FRAI24.doc.pdf},\r\n ABSTRACT = {Industrial robotic systems (IRS) consist of industrial \r\n robots that automate industrial processes. They accurately perform \r\n repetitive tasks, replacing or assisting with dangerous jobs like \r\n assembly in the automotive and chemical industries. Failures in these \r\n systems can be catastrophic, so it is important to ensure their \r\n quality and safety before using them. One way to do this is by \r\n applying a software testing process to find faults before they become \r\n failures. However, software testing in industrial robotic systems has \r\n some challenges. These include differences in perspectives on \r\n software testing from people with diverse backgrounds, coordinating \r\n and collaborating with diverse teams, and performing software testing \r\n within the complex integration inherent in industrial environments. \r\n In traditional systems, a well-known development process uses simple, \r\n structured sentences in English to facilitate communication between \r\n project team members and business stakeholders. This process is \r\n called behavior-driven development (BDD), and one of its pillars is \r\n the use of templates to write user stories, scenarios, and automated \r\n acceptance tests. We propose a software testing (ST) approach called \r\n automated acceptance testing for industrial robotic systems (AAT4IRS) \r\n that uses natural language to write the features and scenarios to be \r\n tested. We evaluated our ST approach through a proof-of-concept, \r\n performing a pick-and-place process and applying mutation testing to \r\n measure its effectiveness. The results show that the test suites \r\n implemented using AAT4IRS were highly effective, with 79% of the \r\n generated mutants detected, thus instilling confidence in the \r\n robustness of our approach.}\r\n}\r\n\r\n","author_short":["Dos Santos, M. G.","Petrillo, F.","Hall�, S.","Gu�h�neuc, Y."],"editor_short":["Kyriakopoulos, K. 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