Finding bugs is easy

By David Hovemeyer, William W. Pugh. SIGP, 2004

948 citations recorded by the source corpus.

Subfields: Information Systems, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/24f9bbadcfbddad0bec4a89aa2df5bb10f39fb97

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Citations in the corpus

A comparison of bug finding tools for Java (2004, methodology): “FindBugs [13] is a bug pattern detector for Java.”
A Systematic Literature Review of Automated Feedback Generation for Programming Exercises (2018, methodology): “We have found a number of static analysis tools, for example CheckStyle4 for checking code conventions, FindBugs [78]5 for finding bugs, and PMD6 for detecting bad coding practices.”
GenProg: A Generic Method for Automatic Software Repair (2012, methodology): “Techniques for automatically detecting software flaws include intrusion detection [8], model checking and lightweight static analyses [9], [10], and software diversity methods [11], [12].”
Graph-based mining of multiple object usage patterns (2009, background): “FindBugs [14] also looks for specified bug patterns.”
Understanding and detecting real-world performance bugs (2012, background): “Energy efficiency: Increasing energy costs provide a powerful economic argument for avoiding performance bugs.”
Improving bug localization using structured information retrieval (2013, methodology): “FindBug [15] is a popular bug localization tool based on static program analysis that can detect bugs by identifying buggy patterns frequently happened in practice.”
A Study of Android Application Security (2011, methodology): “Hovemeyer and Pugh [22] study six popular Java applications and libraries using FindBugs extended with additional checks.”
On the value of static analysis for fault detection in software (2006, methodology): “In our research, we analyzed software faults and failures and assigned each an ODC category.”
Detecting object usage anomalies (2007, background): “Hovemeyer and Pugh [17] created F IND B UGS , which statically looks for a priori speciﬁed bug patterns.”
A genetic programming approach to automated software repair (2009, background): “Many techniques are available for identifying bugs in programs, both statically (e.g., [7, 16]) and dynamically (e.g., [14, 17, 20]).”
Search Based Software Engineering (2013, neutral): “such as clustering [1,11,20], formal concept analysis [15] or search-based optimization techniques [13,14] to find (near) optimal solutions for such objectives, e.”
Finding application errors and security flaws using PQL: a program query language (2005, neutral): “2, or e nsuring that private data members cannot be modified because referen ces to them escape [22]) to ordering constraints across the life of the entire program (as demonstrated in Sections 5.”
Bugram: Bug detection with n-gram language models (2016, background): “Software bug detection techniques have been shown to improve software reliability by finding previously unknown bugs in mature software projects [13, 16].”
DECOR: A Method for the Specification and Detection of Code and Design Smells (2010, background): “[32] detects correctness and performance-related bugs in JAVA systems.”
Deep Learning to Find Bugs (2017, background): “Manually written bug checkers, such as FindBugs or Error Prone, are based on bug patterns, each of which corresponds to a separately implemented analysis.”
Understanding security mistakes developers make: Qualitative analysis from Build It, Break It, Fix It (2020, methodology): “Moreover, we believe that modern analysis tools and testing techniques [6,7,13,14,23,27,37, 40,43,70,71,81] should uncover many of them.”
Learning a Metric for Code Readability (2010, background): “Specifically, we first test for a correlation between readability and FindBugs, a popular static bug-finding tool [19].”