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Automating debugging through data mining
KTH, School of Technology and Health (STH), Medical Engineering, Computer and Electronic Engineering.
KTH, School of Technology and Health (STH), Medical Engineering, Computer and Electronic Engineering.
2017 (English)Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesisAlternative title
Automatisering av felsökning genom data mining (Swedish)
Abstract [en]

Contemporary technological systems generate massive quantities of log messages. These messages can be stored, searched and visualized efficiently using log management and analysis tools. The analysis of log messages offer insights into system behavior such as performance, server status and execution faults in web applications.

iStone AB wants to explore the possibility to automate their debugging process. Since iStone does most parts of their debugging manually, it takes time to find errors within the system. The aim was therefore to find different solutions to reduce the time it takes to debug.

An analysis of log messages within access – and console logs were made, so that the most appropriate data mining techniques for iStone’s system would be chosen. Data mining algorithms and log management and analysis tools were compared. The result of the comparisons showed that the ELK Stack as well as a mixture between Eclat and a hybrid algorithm (Eclat and Apriori) were the most appropriate choices. To demonstrate their feasibility, the ELK Stack and Eclat were implemented. The produced results show that data mining and the use of a platform for log analysis can facilitate and reduce the time it takes to debug.

Abstract [sv]

Dagens system genererar stora mängder av loggmeddelanden. Dessa meddelanden kan effektivt lagras, sökas och visualiseras genom att använda sig av logghanteringsverktyg. Analys av loggmeddelanden ger insikt i systemets beteende såsom prestanda, serverstatus och exekveringsfel som kan uppkomma i webbapplikationer.

iStone AB vill undersöka möjligheten att automatisera felsökning. Eftersom iStone till mestadels utför deras felsökning manuellt så tar det tid att hitta fel inom systemet. Syftet var att därför att finna olika lösningar som reducerar tiden det tar att felsöka.

En analys av loggmeddelanden inom access – och konsolloggar utfördes för att välja de mest lämpade data mining tekniker för iStone’s system. Data mining algoritmer och logghanteringsverktyg jämfördes. Resultatet av jämförelserna visade att ELK Stacken samt en blandning av Eclat och en hybrid algoritm (Eclat och Apriori) var de lämpligaste valen. För att visa att så är fallet så implementerades ELK Stacken och Eclat. De framställda resultaten visar att data mining och användning av en plattform för logganalys kan underlätta och minska den tid det tar för att felsöka.

Place, publisher, year, edition, pages
2017. , 52 p.
Series
TRITA-STH, 2017:23
Keyword [en]
Association rule mining, Machine learning, Classification algorithms, Supervised learning, Text mining, Log management and analysis tools
Keyword [sv]
Association rule mining, Maskininlärning, Classification algorithms, Supervised learning, Text mining, Logghanteringsverktyg
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-203244OAI: oai:DiVA.org:kth-203244DiVA: diva2:1081737
External cooperation
iStone
Subject / course
Computer Technology, Program- and System Development
Educational program
Bachelor of Science in Engineering - Computer Engineering
Supervisors
Examiners
Available from: 2017-06-16 Created: 2017-03-14 Last updated: 2017-06-16Bibliographically approved

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Automating debugging through data mining(2692 kB)47 downloads
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