NDLI: Enabling forensics by proposing heuristics to identify mandatory log events

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Enabling forensics by proposing heuristics to identify mandatory log events

Content Provider	ACM Digital Library
Author	Pandita, Rahul King, Jason Williams, Laurie
Abstract	Software engineers often implement logging mechanisms to debug software and diagnose faults. As modern software manages increasingly sensitive data, logging mechanisms also need to capture detailed traces of user activity to enable forensics and hold users accountable. Existing techniques for identifying what events to log are often subjective and produce inconsistent results. The objective of this study is to help software engineers strengthen forensic-ability and user accountability by 1) systematically identifying mandatory log events through processing of unconstrained natural language software artifacts; and 2) proposing empirically-derived heuristics to help determine whether an event must be logged. We systematically extract each verb and object being acted upon from natural language software artifacts for three open-source software systems. We extract 3,513 verb-object pairs from 2,128 total sentences studied. Two raters classify each verb-object pair as either a mandatory log event or not. Through grounded theory analysis of discussions to resolve disagreements between the two raters, we develop 12 heuristics to help determine whether a verb-object pair describes an action that must be logged. Our heuristics help resolve 882 (96%) of 919 disagreements between the two raters. In addition, our results demonstrate that the proposed heuristics facilitate classification of 3,372 (96%) of 3,513 extracted verb-object pairs as either mandatory log events or not.
Starting Page	1
Ending Page	11
Page Count	11
File Format	PDF
ISBN	9781450333764
DOI	10.1145/2746194.2746200
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-04-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Logging Forensics Accountability Nonrepudiation Natural language Security Software requirements
Content Type	Text
Resource Type	Article

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