@ARTICLE{ElMasri20-IST-SLRonALATs,
   AUTHOR       = {El Masri, Diana and F�bio Petrillo and 
      Yann-Ga�l Gu�h�neuc and Abdelwahab Hamou-Lhadj and Anas Bouziane},
   JOURNAL      = {Information and Software Technology (IST)},
   TITLE        = {A Systematic Literature Review on Automated Log 
      Abstraction Techniques},
   YEAR         = {2020},
   MONTH        = {June},
   NOTE         = {23 pages.},
   OPTNUMBER    = {},
   PAGES        = {106276},
   VOLUME       = {122},
   EDITOR       = {G�nther Ruhe},
   KEYWORDS     = {Topic: <b>Quality models</b>, 
      Rubrique : <b>mod�les de qualit�</b>, Journal: <b>IST</b>},
   PUBLISHER    = {Elsevier},
   URL          = {http://www.ptidej.net/publications/documents/IST20b.doc.pdf},
   ABSTRACT     = {Context: Logs are often the first and only information 
      available to software engineers to understand and debug their 
      systems. Automated log-analysis techniques help software engineers 
      gain insights into large log data. These techniques have several 
      steps, among which log abstraction is the most important because it 
      transforms raw log-data into high-level information. Thus, log 
      abstraction allows software engineers to perform further analyses. 
      Existing log-abstraction techniques vary significantly in their 
      designs and performances. To the best of our knowledge, there is no 
      study that examines the performances of these techniques with respect 
      to the following seven quality aspects concurrently: mode, coverage, 
      delimiter independence, efficiency,scalability, system knowledge 
      independence, and parameter tuning effort. Objectives: We want (1) to 
      build a quality model for evaluating automated log-abstraction 
      techniques and (2) to evaluate and recommend existing automated 
      log-abstraction techniques using this quality model. Method: We 
      perform a systematic literature review (SLR) of automated 
      log-abstraction techniques. We review 89 research papers out of 2,864 
      initial papers. Results: Through this SLR, we (1) identify 17 
      automated log-abstraction techniques, (2) build a quality model 
      composed of seven desirable aspects: mode, coverage, delimiter 
      independence, efficiency, scalability, system knowledge independence, 
      and parameter tuning effort, and (3) make recommendations for 
      researchers on future research directions. Conclusion: Our quality 
      model and recommendations help researchers learn about the 
      state-of-the-art automated log-abstraction techniques, identify 
      research gaps to enhance existing techniques, and develop new ones. 
      We also support software engineers in understanding the advantages 
      and limitations of existing techniques and in choosing the suitable 
      technique to their unique use cases.}
}

{"_id":"569FRSL66ayjYtd3Q","bibbaseid":"elmasri-petrillo-guhneuc-hamoulhadj-bouziane-asystematicliteraturereviewonautomatedlogabstractiontechniques-2020","authorIDs":["AfJhKcg96muyPdu7S"],"author_short":["El Masri, D.","Petrillo, F.","Gu�h�neuc, Y.","Hamou-Lhadj, A.","Bouziane, A."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["El","Masri"],"firstnames":["Diana"],"suffixes":[]},{"firstnames":["F�bio"],"propositions":[],"lastnames":["Petrillo"],"suffixes":[]},{"firstnames":["Yann-Ga�l"],"propositions":[],"lastnames":["Gu�h�neuc"],"suffixes":[]},{"firstnames":["Abdelwahab"],"propositions":[],"lastnames":["Hamou-Lhadj"],"suffixes":[]},{"firstnames":["Anas"],"propositions":[],"lastnames":["Bouziane"],"suffixes":[]}],"journal":"Information and Software Technology (IST)","title":"A Systematic Literature Review on Automated Log Abstraction Techniques","year":"2020","month":"June","note":"23 pages.","optnumber":"","pages":"106276","volume":"122","editor":[{"firstnames":["G�nther"],"propositions":[],"lastnames":["Ruhe"],"suffixes":[]}],"keywords":"Topic: <b>Quality models</b>, Rubrique : <b>mod�les de qualit�</b>, Journal: <b>IST</b>","publisher":"Elsevier","url":"http://www.ptidej.net/publications/documents/IST20b.doc.pdf","abstract":"Context: Logs are often the first and only information available to software engineers to understand and debug their systems. Automated log-analysis techniques help software engineers gain insights into large log data. These techniques have several steps, among which log abstraction is the most important because it transforms raw log-data into high-level information. Thus, log abstraction allows software engineers to perform further analyses. Existing log-abstraction techniques vary significantly in their designs and performances. To the best of our knowledge, there is no study that examines the performances of these techniques with respect to the following seven quality aspects concurrently: mode, coverage, delimiter independence, efficiency,scalability, system knowledge independence, and parameter tuning effort. Objectives: We want (1) to build a quality model for evaluating automated log-abstraction techniques and (2) to evaluate and recommend existing automated log-abstraction techniques using this quality model. Method: We perform a systematic literature review (SLR) of automated log-abstraction techniques. We review 89 research papers out of 2,864 initial papers. Results: Through this SLR, we (1) identify 17 automated log-abstraction techniques, (2) build a quality model composed of seven desirable aspects: mode, coverage, delimiter independence, efficiency, scalability, system knowledge independence, and parameter tuning effort, and (3) make recommendations for researchers on future research directions. Conclusion: Our quality model and recommendations help researchers learn about the state-of-the-art automated log-abstraction techniques, identify research gaps to enhance existing techniques, and develop new ones. We also support software engineers in understanding the advantages and limitations of existing techniques and in choosing the suitable technique to their unique use cases.","bibtex":"@ARTICLE{ElMasri20-IST-SLRonALATs,\r\n AUTHOR = {El Masri, Diana and F�bio Petrillo and \r\n Yann-Ga�l Gu�h�neuc and Abdelwahab Hamou-Lhadj and Anas Bouziane},\r\n JOURNAL = {Information and Software Technology (IST)},\r\n TITLE = {A Systematic Literature Review on Automated Log \r\n Abstraction Techniques},\r\n YEAR = {2020},\r\n MONTH = {June},\r\n NOTE = {23 pages.},\r\n OPTNUMBER = {},\r\n PAGES = {106276},\r\n VOLUME = {122},\r\n EDITOR = {G�nther Ruhe},\r\n KEYWORDS = {Topic: <b>Quality models</b>, \r\n Rubrique : <b>mod�les de qualit�</b>, Journal: <b>IST</b>},\r\n PUBLISHER = {Elsevier},\r\n URL = {http://www.ptidej.net/publications/documents/IST20b.doc.pdf},\r\n ABSTRACT = {Context: Logs are often the first and only information \r\n available to software engineers to understand and debug their \r\n systems. Automated log-analysis techniques help software engineers \r\n gain insights into large log data. These techniques have several \r\n steps, among which log abstraction is the most important because it \r\n transforms raw log-data into high-level information. Thus, log \r\n abstraction allows software engineers to perform further analyses. \r\n Existing log-abstraction techniques vary significantly in their \r\n designs and performances. To the best of our knowledge, there is no \r\n study that examines the performances of these techniques with respect \r\n to the following seven quality aspects concurrently: mode, coverage, \r\n delimiter independence, efficiency,scalability, system knowledge \r\n independence, and parameter tuning effort. Objectives: We want (1) to \r\n build a quality model for evaluating automated log-abstraction \r\n techniques and (2) to evaluate and recommend existing automated \r\n log-abstraction techniques using this quality model. Method: We \r\n perform a systematic literature review (SLR) of automated \r\n log-abstraction techniques. We review 89 research papers out of 2,864 \r\n initial papers. Results: Through this SLR, we (1) identify 17 \r\n automated log-abstraction techniques, (2) build a quality model \r\n composed of seven desirable aspects: mode, coverage, delimiter \r\n independence, efficiency, scalability, system knowledge independence, \r\n and parameter tuning effort, and (3) make recommendations for \r\n researchers on future research directions. Conclusion: Our quality \r\n model and recommendations help researchers learn about the \r\n state-of-the-art automated log-abstraction techniques, identify \r\n research gaps to enhance existing techniques, and develop new ones. \r\n We also support software engineers in understanding the advantages \r\n and limitations of existing techniques and in choosing the suitable \r\n technique to their unique use cases.}\r\n}\r\n\r\n","author_short":["El Masri, D.","Petrillo, F.","Gu�h�neuc, Y.","Hamou-Lhadj, A.","Bouziane, A."],"editor_short":["Ruhe, G."],"key":"ElMasri20-IST-SLRonALATs","id":"ElMasri20-IST-SLRonALATs","bibbaseid":"elmasri-petrillo-guhneuc-hamoulhadj-bouziane-asystematicliteraturereviewonautomatedlogabstractiontechniques-2020","role":"author","urls":{"Paper":"http://www.ptidej.net/publications/documents/IST20b.doc.pdf"},"keyword":["Topic: <b>Quality models</b>","Rubrique : <b>mod�les de qualit�</b>","Journal: <b>IST</b>"],"metadata":{"authorlinks":{"gu�h�neuc, y":"https://bibbase.org/show?bib=http%3A%2F%2Fwww.yann-gael.gueheneuc.net%2FWork%2FPublications%2FBiblio%2Fcomplete-bibliography.bib&msg=embed"}},"downloads":0},"bibtype":"article","biburl":"http://www.yann-gael.gueheneuc.net/Work/Publications/Biblio/complete-bibliography.bib","creationDate":"2020-09-16T17:55:32.064Z","downloads":0,"keywords":["topic: <b>quality models</b>","rubrique : <b>mod�les de qualit�</b>","journal: <b>ist</b>"],"search_terms":["systematic","literature","review","automated","log","abstraction","techniques","el masri","petrillo","gu�h�neuc","hamou-lhadj","bouziane"],"title":"A Systematic Literature Review on Automated Log Abstraction Techniques","year":2020,"dataSources":["KfXmPhaoCuAwjS2aX","SCHo5ZZkXuxNNaMME","8vn5MSGYWB4fAx9Z4"]}