WYSIWIB: exploiting fine-grained program structure in a scriptable API-usage protocol-finding process
SUMMARY Bug‐finding tools rely on specifications of what is correct or incorrect code. As it is difficult for a tool developer or user to anticipate all possible specifications, strategies for inferring specifications have been proposed. These strategies obtain probable specifications by observing c...
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| Vydáno v: | Software, practice & experience Ročník 43; číslo 1; s. 67 - 92 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Chichester, UK
John Wiley & Sons, Ltd
01.01.2013
Wiley Subscription Services, Inc Wiley |
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| ISSN: | 0038-0644, 1097-024X |
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| Abstract | SUMMARY
Bug‐finding tools rely on specifications of what is correct or incorrect code. As it is difficult for a tool developer or user to anticipate all possible specifications, strategies for inferring specifications have been proposed. These strategies obtain probable specifications by observing common characteristics of code or execution traces, typically focusing on sequences of function calls. To counter the observed high rate of false positives, heuristics have been proposed for ranking or pruning the results. These heuristics, however, can result in false negatives, especially for rarely used functions. In this paper, we propose an alternate approach to specification inference, in which the user guides the inference process using patterns of code that reflect the user's understanding of the conventions and design of the targeted software project. We focus on specifications describing the correct usage of API functions, which we refer to as API protocols. Our approach builds on the Coccinelle program matching and transformation tool, which allows a user to construct patterns that reflect the structure of the code to be matched. We evaluate our approach on the source code of the Linux kernel, which defines a very large number of API functions with varying properties. Linux is also critical software, implying that fixing even bugs involving rarely used protocols is essential. In our experiments, we use our approach to find over 3000 potential API protocols, with an estimated false positive rate of under 15% and use these protocols to find over 360 bugs in the use of API functions. Copyright © 2012 John Wiley & Sons, Ltd. |
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| AbstractList | SUMMARY Bug-finding tools rely on specifications of what is correct or incorrect code. As it is difficult for a tool developer or user to anticipate all possible specifications, strategies for inferring specifications have been proposed. These strategies obtain probable specifications by observing common characteristics of code or execution traces, typically focusing on sequences of function calls. To counter the observed high rate of false positives, heuristics have been proposed for ranking or pruning the results. These heuristics, however, can result in false negatives, especially for rarely used functions. In this paper, we propose an alternate approach to specification inference, in which the user guides the inference process using patterns of code that reflect the user's understanding of the conventions and design of the targeted software project. We focus on specifications describing the correct usage of API functions, which we refer to as API protocols. Our approach builds on the Coccinelle program matching and transformation tool, which allows a user to construct patterns that reflect the structure of the code to be matched. We evaluate our approach on the source code of the Linux kernel, which defines a very large number of API functions with varying properties. Linux is also critical software, implying that fixing even bugs involving rarely used protocols is essential. In our experiments, we use our approach to find over 3000 potential API protocols, with an estimated false positive rate of under 15% and use these protocols to find over 360 bugs in the use of API functions. Copyright © 2012 John Wiley & Sons, Ltd. [PUBLICATION ABSTRACT] SUMMARY Bug-finding tools rely on specifications of what is correct or incorrect code. As it is difficult for a tool developer or user to anticipate all possible specifications, strategies for inferring specifications have been proposed. These strategies obtain probable specifications by observing common characteristics of code or execution traces, typically focusing on sequences of function calls. To counter the observed high rate of false positives, heuristics have been proposed for ranking or pruning the results. These heuristics, however, can result in false negatives, especially for rarely used functions. In this paper, we propose an alternate approach to specification inference, in which the user guides the inference process using patterns of code that reflect the user's understanding of the conventions and design of the targeted software project. We focus on specifications describing the correct usage of API functions, which we refer to as API protocols. Our approach builds on the Coccinelle program matching and transformation tool, which allows a user to construct patterns that reflect the structure of the code to be matched. We evaluate our approach on the source code of the Linux kernel, which defines a very large number of API functions with varying properties. Linux is also critical software, implying that fixing even bugs involving rarely used protocols is essential. In our experiments, we use our approach to find over 3000 potential API protocols, with an estimated false positive rate of under 15% and use these protocols to find over 360 bugs in the use of API functions. Copyright [copy 2012 John Wiley & Sons, Ltd. Bug‐finding tools rely on specifications of what is correct or incorrect code. As it is difficult for a tool developer or user to anticipate all possible specifications, strategies for inferring specifications have been proposed. These strategies obtain probable specifications by observing common characteristics of code or execution traces, typically focusing on sequences of function calls. To counter the observed high rate of false positives, heuristics have been proposed for ranking or pruning the results. These heuristics, however, can result in false negatives, especially for rarely used functions. In this paper, we propose an alternate approach to specification inference, in which the user guides the inference process using patterns of code that reflect the user's understanding of the conventions and design of the targeted software project. We focus on specifications describing the correct usage of API functions, which we refer to as API protocols. Our approach builds on the Coccinelle program matching and transformation tool, which allows a user to construct patterns that reflect the structure of the code to be matched. We evaluate our approach on the source code of the Linux kernel, which defines a very large number of API functions with varying properties. Linux is also critical software, implying that fixing even bugs involving rarely used protocols is essential. In our experiments, we use our approach to find over 3000 potential API protocols, with an estimated false positive rate of under 15% and use these protocols to find over 360 bugs in the use of API functions. Copyright © 2012 John Wiley & Sons, Ltd. Bug-finding tools rely on specifications of what is correct or incorrect code. As it is difficult for a tool developer or user to anticipate all possible specifications, strategies for inferring specifications have been proposed. These strategies obtain probable specifications by observing common characteristics of code or execution traces, typically focusing on sequences of function calls. To counter the observed high rate of false positives, heuristics have been proposed for ranking or pruning the results. These heuristics, however, can result in false negatives, especially for rarely used functions. In this paper, we propose an alternate approach to specification inference, in which the user guides the inference process using patterns of code that reflect the user's understanding of the conventions and design of the targeted software project. We focus on specifications describing the correct usage of API functions, which we refer to as API protocols. Our approach builds on the Coccinelle program matching and transformation tool, which allows a user to construct patterns that reflect the structure of the code to be matched. We evaluate our approach on the source code of the Linux kernel, which defines a very large number of API functions with varying properties. Linux is also critical software, implying that fixing even bugs involving rarely used protocols is essential. In our experiments, we use our approach to find over 3000 potential API protocols, with an estimated false positive rate of under 15% and use these protocols to find over 360 bugs in the use of API functions. SUMMARY Bug‐finding tools rely on specifications of what is correct or incorrect code. As it is difficult for a tool developer or user to anticipate all possible specifications, strategies for inferring specifications have been proposed. These strategies obtain probable specifications by observing common characteristics of code or execution traces, typically focusing on sequences of function calls. To counter the observed high rate of false positives, heuristics have been proposed for ranking or pruning the results. These heuristics, however, can result in false negatives, especially for rarely used functions. In this paper, we propose an alternate approach to specification inference, in which the user guides the inference process using patterns of code that reflect the user's understanding of the conventions and design of the targeted software project. We focus on specifications describing the correct usage of API functions, which we refer to as API protocols. Our approach builds on the Coccinelle program matching and transformation tool, which allows a user to construct patterns that reflect the structure of the code to be matched. We evaluate our approach on the source code of the Linux kernel, which defines a very large number of API functions with varying properties. Linux is also critical software, implying that fixing even bugs involving rarely used protocols is essential. In our experiments, we use our approach to find over 3000 potential API protocols, with an estimated false positive rate of under 15% and use these protocols to find over 360 bugs in the use of API functions. Copyright © 2012 John Wiley & Sons, Ltd. |
| Author | Palix, Nicolas Brunel, Julien Muller, Gilles Lawall, Julia L. Hansen, René Rydhof Stuart, Henrik |
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| Keywords | Linux bug finding program analysis |
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| Notes | This is an extended version of the article 'WYSIWIB: A Declarative Approach to Finding API Protocols and Bugs in Linux Code' that was presented at the 2009 IEEE/IFIP International Conference on Dependable Systems and Networks. ArticleID:SPE2102 istex:3147194B8E9F5B02960E7325CCF445DD58EF24CB ark:/67375/WNG-78P5RH23-K This is an extended version of the article ‘WYSIWIB: A Declarative Approach to Finding API Protocols and Bugs in Linux Code’ that was presented at the 2009 IEEE/IFIP International Conference on Dependable Systems and Networks. Julien Brunel's current address: ONERA, Toulouse, France ObjectType-Article-1 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Feature-2 content type line 23 |
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| References | Ayewah N, Hovemeyer D, Morgenthaler JD, Penix J, Pugh W. Using static analysis to find bugs. IEEE Software 2008; 25(5):22-29. MacKenzie D, Eggert P, Stallman R. Comparing and Merging Files with GNU Diff and Patch. Network Theory Ltd. UK, Jan. 2003. Unified Format section, (Available from: http://www.gnu.org/software/diffutils/manual/html_node/Unified-Format.html). 2008; 25 2009 2008 2007 2006 2005 2003 2001 2000 2011 2010 Kremenek T (e_1_2_13_7_1) 2006 Ball T (e_1_2_13_3_1) 2006 e_1_2_13_15_1 MacKenzie D (e_1_2_13_13_1) 2003 e_1_2_13_16_1 e_1_2_13_11_1 e_1_2_13_22_1 e_1_2_13_9_1 Engler DR (e_1_2_13_5_1) 2000 e_1_2_13_8_1 Ramanathan MK (e_1_2_13_17_1) 2007 Lo D (e_1_2_13_12_1) 2008 Tan L (e_1_2_13_20_1) 2007 Olesen MC (e_1_2_13_14_1) 2010 Stuart H (e_1_2_13_19_1) 2007 Brunel J (e_1_2_13_4_1) 2009 Yang J (e_1_2_13_23_1) 2006 e_1_2_13_2_1 Tan L (e_1_2_13_21_1) 2008 Engler DR (e_1_2_13_6_1) 2001 Li Z (e_1_2_13_10_1) 2005 e_1_2_13_18_1 |
| References_xml | – reference: Ayewah N, Hovemeyer D, Morgenthaler JD, Penix J, Pugh W. Using static analysis to find bugs. IEEE Software 2008; 25(5):22-29. – reference: MacKenzie D, Eggert P, Stallman R. Comparing and Merging Files with GNU Diff and Patch. Network Theory Ltd. UK, Jan. 2003. Unified Format section, (Available from: http://www.gnu.org/software/diffutils/manual/html_node/Unified-Format.html). – start-page: 292 year: 2009 end-page: 306 – start-page: 240 year: 2007 end-page: 250 – start-page: 50 year: 2008 end-page: 56 – start-page: 379 year: 2008 end-page: 394 – start-page: 114 year: 2009 end-page: 126 – year: 2008 – year: 2007 – year: 2003 – start-page: 305 year: 2011 end-page: 318 – start-page: 247 year: 2008 end-page: 260 – start-page: 43 year: 2009 end-page: 52 – start-page: 145 year: 2007 end-page: 158 – start-page: 461 year: 2005 end-page: 476 – start-page: 73 year: 2006 end-page: 85 – start-page: 306 year: 2005 end-page: 315 – start-page: 57 year: 2001 end-page: 72 – start-page: 282 year: 2006 end-page: 291 – start-page: 1 year: 2000 end-page: 16 – volume: 25 start-page: 22 issue: 5 year: 2008 end-page: 29 article-title: Using static analysis to find bugs publication-title: IEEE Software – start-page: 161 year: 2006 end-page: 176 – year: 2010 – ident: e_1_2_13_8_1 doi: 10.1109/DSN.2009.5270354 – start-page: 240 volume-title: 29th International Conference on Software Engineering year: 2007 ident: e_1_2_13_17_1 – volume-title: 4th Workshop on Programming Languages and Operating Systems (PLOS 2007) year: 2007 ident: e_1_2_13_19_1 – start-page: 282 volume-title: 28th International Conference on Software Engineering (ICSE) year: 2006 ident: e_1_2_13_23_1 – start-page: 57 volume-title: Proceedings of the 18th ACM Symposium on Operating System Principles year: 2001 ident: e_1_2_13_6_1 – ident: e_1_2_13_16_1 doi: 10.1145/1950365.1950401 – start-page: 50 volume-title: International Workshop on Dynamic Analysis year: 2008 ident: e_1_2_13_12_1 – start-page: 1 volume-title: Fourth USENIX Symposium on Operating Systems Design and Implementation (OSDI) year: 2000 ident: e_1_2_13_5_1 – ident: e_1_2_13_22_1 doi: 10.1007/978-3-540-31980-1_30 – ident: e_1_2_13_11_1 – start-page: 145 volume-title: Proceedings of the 21st ACM Symposium on Operating System Principles year: 2007 ident: e_1_2_13_20_1 – ident: e_1_2_13_2_1 doi: 10.1109/MS.2008.130 – ident: e_1_2_13_18_1 – start-page: 161 volume-title: OSDI year: 2006 ident: e_1_2_13_7_1 – volume-title: 4th International Workshop on Foundations and Techniques for Open Source Software Certification year: 2010 ident: e_1_2_13_14_1 – ident: e_1_2_13_15_1 doi: 10.1145/1352592.1352618 – start-page: 306 volume-title: Proceedings of the 10th European Software Engineering Conference held jointly with the 13th ACM SIGSOFT International Symposium on Foundations of Software Engineering year: 2005 ident: e_1_2_13_10_1 – ident: e_1_2_13_9_1 doi: 10.1007/978-3-642-00768-2_26 – start-page: 73 volume-title: The first ACM SIGOPS EuroSys conference (EuroSys 2006) year: 2006 ident: e_1_2_13_3_1 doi: 10.1145/1217935.1217943 – volume-title: Comparing and Merging Files with GNU Diff and Patch year: 2003 ident: e_1_2_13_13_1 – start-page: 379 volume-title: USENIX Security Symposium year: 2008 ident: e_1_2_13_21_1 – start-page: 114 volume-title: The 36th Annual ACM SIGPLAN‐SIGACT Symposium on Principles of Programming Languages year: 2009 ident: e_1_2_13_4_1 doi: 10.1145/1480881.1480897 |
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Bug‐finding tools rely on specifications of what is correct or incorrect code. As it is difficult for a tool developer or user to anticipate all... Bug‐finding tools rely on specifications of what is correct or incorrect code. As it is difficult for a tool developer or user to anticipate all possible... SUMMARY Bug-finding tools rely on specifications of what is correct or incorrect code. As it is difficult for a tool developer or user to anticipate all... Bug-finding tools rely on specifications of what is correct or incorrect code. As it is difficult for a tool developer or user to anticipate all possible... |
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| SubjectTerms | API bug finding Computer programs Computer Science Construction equipment Heuristic Inference Linux Operating Systems program analysis Software Software Engineering Specifications Strategy |
| Title | WYSIWIB: exploiting fine-grained program structure in a scriptable API-usage protocol-finding process |
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