@ARTICLE{ElMasri20-IST-SLRonALATs,
   AUTHOR       = {Diana El Masri 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>, Venue: <b>IST</b>},
   PUBLISHER    = {Elsevier},
   URL          = {http://www.ptidej.net/publications/documents/IST20b.doc.pdf},
   ABSTRACT     = {\textbf{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. 
      \textbf{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. \textbf{Method:} We perform a systematic 
      literature review (SLR) of automated log-abstraction techniques. We 
      review 89 research papers out of 2,864 initial papers. 
      \textbf{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. \textbf{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":"WxicqiWYa4EzhuSNk","bibbaseid":"masri-petrillo-guhneuc-hamoulhadj-bouziane-asystematicliteraturereviewonautomatedlogabstractiontechniques-2020","author_short":["Masri, D. E.","Petrillo, F.","Gu�h�neuc, Y.","Hamou-Lhadj, A.","Bouziane, A."],"bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["Diana","El"],"propositions":[],"lastnames":["Masri"],"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>, Venue: <b>IST</b>","publisher":"Elsevier","url":"http://www.ptidej.net/publications/documents/IST20b.doc.pdf","abstract":"\\textbfContext: 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. \\textbfObjectives: 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. \\textbfMethod: We perform a systematic literature review (SLR) of automated log-abstraction techniques. We review 89 research papers out of 2,864 initial papers. \\textbfResults: 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. \\textbfConclusion: 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 = {Diana El Masri 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>, Venue: <b>IST</b>},\r\n PUBLISHER = {Elsevier},\r\n URL = {http://www.ptidej.net/publications/documents/IST20b.doc.pdf},\r\n ABSTRACT = {\\textbf{Context:} Logs are often the first and only \r\n information available to software engineers to understand and debug \r\n their systems. Automated log-analysis techniques help software \r\n engineers gain insights into large log data. These techniques have \r\n several steps, among which log abstraction is the most important \r\n because it transforms raw log-data into high-level information. Thus, \r\n log abstraction allows software engineers to perform further \r\n analyses. Existing log-abstraction techniques vary significantly in \r\n their designs and performances. To the best of our knowledge, there \r\n is no study that examines the performances of these techniques with \r\n respect to the following seven quality aspects concurrently: mode, \r\n coverage, delimiter independence, efficiency,scalability, system \r\n knowledge independence, and parameter tuning effort. \r\n \\textbf{Objectives:} We want (1) to build a quality model for \r\n evaluating automated log-abstraction techniques and (2) to evaluate \r\n and recommend existing automated log-abstraction techniques using \r\n this quality model. \\textbf{Method:} We perform a systematic \r\n literature review (SLR) of automated log-abstraction techniques. We \r\n review 89 research papers out of 2,864 initial papers. \r\n \\textbf{Results:} Through this SLR, we (1) identify 17 automated \r\n log-abstraction techniques, (2) build a quality model composed of \r\n seven desirable aspects: mode, coverage, delimiter independence, \r\n efficiency, scalability, system knowledge independence, and parameter \r\n tuning effort, and (3) make recommendations for researchers on future \r\n research directions. \\textbf{Conclusion:} Our quality model and \r\n recommendations help researchers learn about the state-of-the-art \r\n automated log-abstraction techniques, identify research gaps to \r\n enhance existing techniques, and develop new ones. We also support \r\n software engineers in understanding the advantages and limitations of \r\n existing techniques and in choosing the suitable technique to their \r\n unique use cases.}\r\n}\r\n\r\n","author_short":["Masri, D. E.","Petrillo, F.","Gu�h�neuc, Y.","Hamou-Lhadj, A.","Bouziane, A."],"editor_short":["Ruhe, G."],"key":"ElMasri20-IST-SLRonALATs","id":"ElMasri20-IST-SLRonALATs","bibbaseid":"masri-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>","Venue: <b>IST</b>"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"http://www.yann-gael.gueheneuc.net/Work/Publications/Biblio/complete-bibliography.bib","dataSources":["8vn5MSGYWB4fAx9Z4"],"keywords":["topic: <b>quality models</b>","venue: <b>ist</b>"],"search_terms":["systematic","literature","review","automated","log","abstraction","techniques","masri","petrillo","gu�h�neuc","hamou-lhadj","bouziane"],"title":"A Systematic Literature Review on Automated Log Abstraction Techniques","year":2020}