To log, or not to log: using heuristics to identify mandatory log events – a controlled experiment

To log, or not to log: using heuristics to identify mandatory log events – a controlled experiment Context User activity logs should capture evidence to help answer who, what, when, where, why, and how a security or privacy breach occurred. However, software engineers often implement logging mechanisms that inadequately record mandatory log events (MLEs), user activities that must be logged to enable forensics. Goal The objective of this study is to support security analysts in performing forensic analysis by evaluating the use of a heuristics-driven method for identifying mandatory log events. Method We conducted a controlled experiment with 103 computer science students enrolled in a graduate-level software security course. All subjects were first asked to identify MLEs described in a set of requirements statements during the pre-period task. In the post-period task, subjects were randomly assigned statements from one type of software artifact (traditional requirements, use-case-based requirements, or user manual), one readability score (simple or complex), and one method (standards-, resource-, or heuristics-driven). We evaluated subject performance using three metrics: statement classification correctness (values from 0 to 1), Communicated by: Richard Paige, Jordi Cabot and Neil Ernst * Jason King jtking@ncsu.edu Jon Stallings jwstalli@ncsu.edu Maria Riaz mriaz@ncsu.edu Laurie Williams laurie_williams@ncsu.edu Department of Computer Science, North Carolina State University, 890 Oval Dr., Raleigh, NC 27695-8206, USA Department http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Empirical Software Engineering Springer Journals

To log, or not to log: using heuristics to identify mandatory log events – a controlled experiment

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Publisher
Springer US
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Computer Science; Software Engineering/Programming and Operating Systems; Programming Languages, Compilers, Interpreters
ISSN
1382-3256
eISSN
1573-7616
D.O.I.
10.1007/s10664-016-9449-1
Publisher site
See Article on Publisher Site

Abstract

Context User activity logs should capture evidence to help answer who, what, when, where, why, and how a security or privacy breach occurred. However, software engineers often implement logging mechanisms that inadequately record mandatory log events (MLEs), user activities that must be logged to enable forensics. Goal The objective of this study is to support security analysts in performing forensic analysis by evaluating the use of a heuristics-driven method for identifying mandatory log events. Method We conducted a controlled experiment with 103 computer science students enrolled in a graduate-level software security course. All subjects were first asked to identify MLEs described in a set of requirements statements during the pre-period task. In the post-period task, subjects were randomly assigned statements from one type of software artifact (traditional requirements, use-case-based requirements, or user manual), one readability score (simple or complex), and one method (standards-, resource-, or heuristics-driven). We evaluated subject performance using three metrics: statement classification correctness (values from 0 to 1), Communicated by: Richard Paige, Jordi Cabot and Neil Ernst * Jason King jtking@ncsu.edu Jon Stallings jwstalli@ncsu.edu Maria Riaz mriaz@ncsu.edu Laurie Williams laurie_williams@ncsu.edu Department of Computer Science, North Carolina State University, 890 Oval Dr., Raleigh, NC 27695-8206, USA Department

Journal

Empirical Software EngineeringSpringer Journals

Published: Aug 24, 2016

References

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