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Near-Omniscient Debugging for Java Using Size-Limited Execution Trace

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dc.contributor.author Shimari, Kazumasa en
dc.contributor.author Ishio, Takashi en
dc.contributor.author Kanda, Tetsuya en
dc.contributor.author Inoue, Katsuro en
dc.date.accessioned 2019-10-08T05:34:10Z en
dc.date.available 2019-10-08T05:34:10Z en
dc.date.issued 2019-10-02 en
dc.identifier.uri http://hdl.handle.net/10061/13396 en
dc.description 35th IEEE International Conference on Software Maintenance and Evolution (ICSME 2019), , Cleveland, OH USA Oct 2019 en
dc.description.abstract Abstract—Logging is an important feature for a software system to record its run-time information. Detailed logging allows developers to collect information in situations where they cannot use an interactive debugger, such as continuous integration and web application server cases. However, extensive logging leads to larger execution traces because few instructions could be repeated many times. To record detailed program behavior within limited storage space constraints, we propose Near-Omniscient Debugging, a methodology that records an execution trace using fixed size buffers for each observed instruction. Our tool monitors a Java program’s execution and annotates source code with observed values in an HTML format. Developers can easily investigate the execution and share the report on a web server. In case of DaCapo benchmark applications, our tool requires fewer than 1% of the complete execution traces to visualize all runtime values used by 66% of instructions that are executed less than 64 times. Developers also can obtain data dependencies with precision 91.8% and recall 79.0% using this tool. Index Terms—Dynamic Analysis, Logging, Software Visualization ja
dc.language.iso en en
dc.publisher IEEE en
dc.rights ©2019 IEEE ja
dc.title Near-Omniscient Debugging for Java Using Size-Limited Execution Trace en
dc.type.nii Conference Paper en
dc.contributor.transcription イシオ, タカシ ja
dc.contributor.alternative 石尾, 隆 ja
dc.textversion author en
dc.identifier.spage 398 en
dc.identifier.epage 401 en
dc.relation.doi 10.1109/ICSME.2019.00068 en
dc.identifier.NAIST-ID 74653577 en


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