Master thesis - Machine learning logging
The purpose of this master thesis is to perform a study and implement a pilot version on how to analyse a log and understand where differences exists from a successful execution.
We can today easily see where error logs are printed, asserts are hitting and crashes occur. When an error has occurred we would like to be able to parse the logs leading up to the error in order to understand differences between a passing and failed execution of a certain test. This includes also logs which appears to be missing from a standard execution.
By using machine learning, a system should be able to learn what is expected from a log by using successful test execution verdicts. Then highlight to the developer which are troubleshooting a failing testcase, where the differences in the log flows exists.
As an example we have looked into different LSTM which are providing the framework to achieve this.
The questions we would like to be answered in this thesis;
- Is this possible to achieve?
- Will this be useful for the developers.
- Once the logs are analysed how can this be used in other contexts, e.g. customer logs.
- Limit the numbers of keys used in analysis.
- Master Thesis student in Computer Science, Software Engineering (or similar).
- Solid understanding of basic data analysis such as anomaly detection, classification, supervised and unsupervised learning
- Linux, Unix work environment.
- One or more scripting languages e.g. python, perl etc.
- Solid understanding and genuine interest in software design and Free and Open Source Software.
- Some basic FOSS handling knowledge and analysis skills.
- A collaborative, self-motivated and driven personality.
- Performance evaluation and documentation skills are required.
- Programming skills in C++ or object oriented design principles.
- Great English skills, The master thesis report shall be written in English.
Mentor: Patrik Olesen, Simon Bood
Hiring manager: Lucas Jönefors
Wanted number of participants: 2-4
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Primary country and city: Sweden (SE) || || Göteborg || Stud&YP
Req ID: 305143