Diffy: Data-Driven Bug Finding for Configurations (2024)

research-article

Open Access

Artifacts Available / v1.1

Diffy: Data-Driven Bug Finding for Configurations (1)

  • Authors:
  • Siva Kesava Reddy Kakarla Microsoft Research, Redmond, USA

    Microsoft Research, Redmond, USA

    Diffy: Data-Driven Bug Finding for Configurations (2)0009-0006-2694-4685

    Search about this author

    ,
  • Francis Y. Yan Microsoft Research, Redmond, USA

    Microsoft Research, Redmond, USA

    Diffy: Data-Driven Bug Finding for Configurations (3)0000-0002-2123-4258

    Search about this author

    ,
  • Ryan Beckett Microsoft Research, Redmond, USA

    Microsoft Research, Redmond, USA

    Diffy: Data-Driven Bug Finding for Configurations (4)0000-0001-7844-2026

    Search about this author

Proceedings of the ACM on Programming LanguagesVolume 8Issue PLDIArticle No.: 155pp 199–222https://doi.org/10.1145/3656385

Published:20 June 2024Publication HistoryDiffy: Data-Driven Bug Finding for Configurations (5)

Related Artifact: Source code for article "Diffy: Data-Driven Bug Finding for Configurations" June 2024softwarehttps://doi.org/10.5281/zenodo.10740687

  • 0citation
  • 0
  • Downloads

Metrics

Total Citations0Total Downloads0

Last 12 Months0

Last 6 weeks0

  • Get Citation Alerts

    New Citation Alert added!

    This alert has been successfully added and will be sent to:

    You will be notified whenever a record that you have chosen has been cited.

    To manage your alert preferences, click on the button below.

    Manage my Alerts

    New Citation Alert!

    Please log in to your account

  • Publisher Site
  • eReader
  • PDF

Skip Abstract Section

Abstract

Configuration errors remain a major cause of system failures and service outages. One promising approach to identify configuration errors automatically is to learn common usage patterns (and anti-patterns) using data-driven methods. However, existing data-driven learning approaches analyze only simple configurations (e.g., those with no hierarchical structure), identify only simple types of issues (e.g., type errors), or require extensive domain-specific tuning. In this paper, we present Diffy, the first push-button configuration analyzer that detects likely bugs in structured configurations. From example configurations, Diffy learns a common template, with "holes" that capture their variation. It then applies unsupervised learning to identify anomalous template parameters as likely bugs. We evaluate Diffy on a large cloud provider's wide-area network, an operational 5G network testbed, and MySQL configurations, demonstrating its versatility, performance, and accuracy. During Diffy's development, it caught and prevented a bug in a configuration timer value that had previously caused an outage for the cloud provider.

Skip Supplemental Material Section

Supplemental Material

Available for Download

zip

pldi24main-p42-p-archive.zip (834 KB)

The PDF in the folder is the full paper with appendices.

References

  1. Ziawasch Abedjan, Lukasz Golab, and Felix Naumann. 2015. Profiling relational data: a survey. The VLDB Journal, 24 (2015), 557–581.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (7)Digital Library
  2. John Backes, Pauline Bolignano, Byron Cook, Catherine Dodge, Andrew Gacek, Kasper Luckow, Neha Rungta, Oksana Tkachuk, and Carsten Varming. 2018. Semantic-based automated reasoning for AWS access policies using SMT. In 2018 Formal Methods in Computer Aided Design (FMCAD). IEEE, Austin, Texas, USA. 1–9.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (9)
  3. Ryan Beckett and Aarti Gupta. 2022. Katra: Realtime Verification for Multilayer Networks. In 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22). USENIX Association, Renton, WA. 617–634. isbn:978-1-939133-27-4 https://www.usenix.org/conference/nsdi22/presentation/beckettGoogle ScholarDiffy: Data-Driven Bug Finding for Configurations (10)
  4. Ryan Beckett, Aarti Gupta, Ratul Mahajan, and David Walker. 2017. A General Approach to Network Configuration Verification. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM ’17). ACM, New York, NY, USA. 155–168. isbn:978-1-4503-4653-5 https://doi.org/10.1145/3098822.3098834Google ScholarDiffy: Data-Driven Bug Finding for Configurations (11)Digital Library
  5. Ryan Beckett, Aarti Gupta, Ratul Mahajan, and David Walker. 2018. Control Plane Compression. In Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication (SIGCOMM ’18). Association for Computing Machinery, New York, NY, USA. 476–489. isbn:9781450355674 https://doi.org/10.1145/3230543.3230583Google ScholarDiffy: Data-Driven Bug Finding for Configurations (13)Digital Library
  6. Ryan Beckett, Aarti Gupta, Ratul Mahajan, and David Walker. 2019. Abstract Interpretation of Distributed Network Control Planes. Proc. ACM Program. Lang., 4, POPL (2019), Article 42, dec, 27 pages. https://doi.org/10.1145/3371110Google ScholarDiffy: Data-Driven Bug Finding for Configurations (15)Digital Library
  7. Tim Bray. 2017. The JavaScript Object Notation (JSON) Data Interchange Format. RFC 8259. https://doi.org/10.17487/RFC8259Google ScholarDiffy: Data-Driven Bug Finding for Configurations (17)Digital Library
  8. Qingrong Chen, Teng Wang, Owolabi Legunsen, Shanshan Li, and Tianyin Xu. 2020. Understanding and discovering software configuration dependencies in cloud and datacenter systems. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2020). Association for Computing Machinery, New York, NY, USA. 362–374. isbn:9781450370431 https://doi.org/10.1145/3368089.3409727Google ScholarDiffy: Data-Driven Bug Finding for Configurations (19)Digital Library
  9. Cisco. 2023. Basic Router Configuration. https://www.cisco.com/c/en/us/td/docs/routers/access/800M/software/800MSCG/routconf.html [Online; accessed 30-March-2023]Google ScholarDiffy: Data-Driven Bug Finding for Configurations (21)
  10. Oracle Corporation. 2023. MySQL. https://www.mysql.com/ Accessed: 2023-11-01Google ScholarDiffy: Data-Driven Bug Finding for Configurations (22)
  11. Min Du and Feifei Li. 2016. Spell: Streaming parsing of system event logs. In 2016 IEEE 16th International Conference on Data Mining (ICDM). 859–864.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (23)Cross Ref
  12. Dawson Engler, David Yu Chen, Seth Hallem, Andy Chou, and Benjamin Chelf. 2001. Bugs as Deviant Behavior: A General Approach to Inferring Errors in Systems Code. In Proceedings of the Eighteenth ACM Symposium on Operating Systems Principles (SOSP ’01). Association for Computing Machinery, New York, NY, USA. 57–72. isbn:1581133898 https://doi.org/10.1145/502034.502041Google ScholarDiffy: Data-Driven Bug Finding for Configurations (25)Digital Library
  13. Dawson Engler, David Yu Chen, Seth Hallem, Andy Chou, and Benjamin Chelf. 2001. Bugs as deviant behavior: A general approach to inferring errors in systems code. ACM SIGOPS Operating Systems Review, 35, 5 (2001), 57–72.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (27)Digital Library
  14. Evolven. 2022. Downtime, Outages and Failures - Understanding Their True Costs. https://www.evolven.com/blog/downtime-outages-and-failures-understanding-their-true-costs.html Accessed: March 26, 2023Google ScholarDiffy: Data-Driven Bug Finding for Configurations (29)
  15. Seyed K. Fayaz, Tushar Sharma, Ari Fogel, Ratul Mahajan, Todd Millstein, Vyas Sekar, and George Varghese. 2016. Efficient Network Reachability Analysis Using a Succinct Control Plane Representation. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, Savannah, GA. 217–232. isbn:978-1-931971-33-1 https://www.usenix.org/conference/osdi16/technical-sessions/presentation/fayazGoogle ScholarDiffy: Data-Driven Bug Finding for Configurations (30)Digital Library
  16. Kathleen Fisher, David Walker, Kenny Q Zhu, and Peter White. 2008. From dirt to shovels: fully automatic tool generation from ad hoc data. Acm sigplan notices, 43, 1 (2008), 421–434.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (32)Digital Library
  17. Ari Fogel, Stanley Fung, Luis Pedrosa, Meg Walraed-Sullivan, Ramesh Govindan, Ratul Mahajan, and Todd Millstein. 2015. A General Approach to Network Configuration Analysis. In 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15). USENIX Association, Oakland, CA. 469–483. isbn:978-1-931971-218 https://www.usenix.org/conference/nsdi15/technical-sessions/presentation/fogelGoogle ScholarDiffy: Data-Driven Bug Finding for Configurations (34)
  18. Cloud Native Computing Foundation. 2023. Kubernetes Documentation. https://kubernetes.io/docs/home/ Accessed: 2023-11-01Google ScholarDiffy: Data-Driven Bug Finding for Configurations (35)
  19. Qiang Fu, Jian-Guang Lou, Yi Wang, and Jiang Li. 2009. Execution anomaly detection in distributed systems through unstructured log analysis. In 2009 ninth IEEE international conference on data mining. 149–158.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (36)
  20. Aaron Gember-Jacobson, Raajay Viswanathan, Aditya Akella, and Ratul Mahajan. 2016. Fast Control Plane Analysis Using an Abstract Representation. In Proceedings of the 2016 ACM SIGCOMM Conference (SIGCOMM ’16). ACM, New York, NY, USA. 300–313. isbn:978-1-4503-4193-6 https://doi.org/10.1145/2934872.2934876Google ScholarDiffy: Data-Driven Bug Finding for Configurations (37)Digital Library
  21. Sumit Gulwani. 2011. Automating string processing in spreadsheets using input-output examples. ACM Sigplan Notices, 46, 1 (2011), 317–330.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (39)Digital Library
  22. Haryadi S Gunawi, Mingzhe Hao, Tanakorn Leesatap*rnwongsa, Tiratat Patana-Anake, Thanh Do, Jeffry Adityatama, Kurnia J Eliazar, Agung Laksono, Jeffrey F Lukman, and Vincentius Martin. 2014. What bugs live in the cloud? a study of 3000+ issues in cloud systems. In Proceedings of the ACM symposium on cloud computing. 1–14.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (41)Digital Library
  23. Songqiao Han, Xiyang Hu, Hailiang Huang, Mingqi Jiang, and Yue Zhao. 2022. ADBench: Anomaly Detection Benchmark. In Neural Information Processing Systems (NeurIPS).Google ScholarDiffy: Data-Driven Bug Finding for Configurations (43)
  24. Pinjia He, Jieming Zhu, Zibin Zheng, and Michael R Lyu. 2017. Drain: An online log parsing approach with fixed depth tree. In 2017 IEEE international conference on web services (ICWS). 33–40.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (44)Cross Ref
  25. Shilin He, Jieming Zhu, Pinjia He, and Michael R Lyu. 2016. Experience report: System log analysis for anomaly detection. In 2016 IEEE 27th international symposium on software reliability engineering (ISSRE). 207–218.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (46)Cross Ref
  26. Alex Horn, Ali Kheradmand, and Mukul Prasad. 2017. Delta-net: Real-time Network Verification Using Atoms. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). USENIX Association, Boston, MA. 735–749. isbn:978-1-931971-37-9 https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/horn-alexGoogle ScholarDiffy: Data-Driven Bug Finding for Configurations (48)Digital Library
  27. 2023. Istio Configuration. https://istio.io/latest/docs/ops/configuration/Google ScholarDiffy: Data-Driven Bug Finding for Configurations (50)
  28. Karthick Jayaraman, Nikolaj Bjorner, Jitu Padhye, Amar Agrawal, Ashish Bhargava, Paul-Andre C Bissonnette, Shane Foster, Andrew Helwer, Mark Kasten, Ivan Lee, Anup Namdhari, Haseeb Niaz, Aniruddha Parkhi, Hanukumar Pinnamraju, Adrian Power, Neha Milind Raje, and Parag Sharma. 2019. Validating Datacenters at Scale. In Proceedings of the ACM Special Interest Group on Data Communication (SIGCOMM ’19). ACM, New York, NY, USA. 200–213. isbn:978-1-4503-5956-6 https://doi.org/10.1145/3341302.3342094Google ScholarDiffy: Data-Driven Bug Finding for Configurations (51)Digital Library
  29. Juniper Networks. 2023. CLI User Guide for Junos OS. https://www.juniper.net/documentation/us/en/software/junos/cli/index.html Accessed: April 2, 2023Google ScholarDiffy: Data-Driven Bug Finding for Configurations (53)
  30. Siva Kesava Reddy Kakarla, Ryan Beckett, Behnaz Arzani, Todd Millstein, and George Varghese. 2020. GRoot: Proactive Verification of DNS Configurations. In Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication (SIGCOMM ’20). Association for Computing Machinery, New York, NY, USA. 310–328. isbn:9781450379557 https://doi.org/10.1145/3387514.3405871Google ScholarDiffy: Data-Driven Bug Finding for Configurations (54)Digital Library
  31. Siva Kesava Reddy Kakarla, Alan Tang, Ryan Beckett, Karthick Jayaraman, Todd Millstein, Yuval Tamir, and George Varghese. 2020. Finding Network Misconfigurations by Automatic Template Inference. In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20). USENIX Association, Santa Clara, CA. 999–1013. isbn:978-1-939133-13-7 https://www.usenix.org/conference/nsdi20/presentation/kakarlaGoogle ScholarDiffy: Data-Driven Bug Finding for Configurations (56)
  32. Siva Kesava Reddy Kakarla, Francis Y. Yan, and Ryan Beckett. 2024. Diffy: Data-Driven Bug Finding for Configurations. https://github.com/microsoft/DiffyConfigAnalyzer Accessed: April 5, 2024Google ScholarDiffy: Data-Driven Bug Finding for Configurations (57)
  33. Siva Kesava Reddy Kakarla, Francis Y. Yan, and Ryan Beckett. 2024. Diffy: Data-Driven Bug Finding for Configurations. https://doi.org/10.5281/zenodo.10740687 Accessed: April 5, 2024Google ScholarDiffy: Data-Driven Bug Finding for Configurations (58)Cross Ref
  34. Peyman Kazemian, George Varghese, and Nick McKeown. 2012. Header Space Analysis: Static Checking for Networks. In 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12). USENIX Association, San Jose, CA. 113–126. isbn:978-931971-92-8 https://www.usenix.org/conference/nsdi12/technical-sessions/presentation/kazemianGoogle ScholarDiffy: Data-Driven Bug Finding for Configurations (60)
  35. Ahmed Khurshid, Xuan Zou, Wenxuan Zhou, Matthew Caesar, and P. Brighten Godfrey. 2013. VeriFlow: Verifying Network-Wide Invariants in Real Time. In Presented as part of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13). USENIX, Lombard, IL. 15–27. isbn:978-1-931971-00-3 https://www.usenix.org/conference/nsdi13/technical-sessions/presentation/khurshidGoogle ScholarDiffy: Data-Driven Bug Finding for Configurations (61)Digital Library
  36. Franck Le, Sihyung Lee, Tina Wong, Hyong S. Kim, and Darrell Newcomb. 2006. Minerals: Using Data Mining to Detect Router Misconfigurations. In Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data (MineNet ’06). Association for Computing Machinery, New York, NY, USA. 293–298. isbn:159593569X https://doi.org/10.1145/1162678.1162681Google ScholarDiffy: Data-Driven Bug Finding for Configurations (63)Digital Library
  37. Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation forest. In 2008 eighth ieee international conference on data mining. IEEE, Pisa, Italy. 413–422.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (65)Digital Library
  38. Hongqiang Harry Liu, Yibo Zhu, Jitu Padhye, Jiaxin Cao, Sri Tallapragada, Nuno P Lopes, Andrey Rybalchenko, Guohan Lu, and Lihua Yuan. 2017. Crystalnet: Faithfully emulating large production networks. In Proceedings of the 26th Symposium on Operating Systems Principles. 599–613.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (67)Digital Library
  39. Nuno P. Lopes, Nikolaj Bjørner, Patrice Godefroid, Karthick Jayaraman, and George Varghese. 2015. Checking Beliefs in Dynamic Networks. In Proceedings of the 12th USENIX Conference on Networked Systems Design and Implementation (NSDI’15). USENIX Association, USA. 499–512. isbn:9781931971218Google ScholarDiffy: Data-Driven Bug Finding for Configurations (69)Digital Library
  40. Haohui Mai, Ahmed Khurshid, Rachit Agarwal, Matthew Caesar, P. Brighten Godfrey, and Samuel Talmadge King. 2011. Debugging the Data Plane with Anteater. SIGCOMM Comput. Commun. Rev., 41, 4 (2011), aug, 290–301. issn:0146-4833 https://doi.org/10.1145/2043164.2018470Google ScholarDiffy: Data-Driven Bug Finding for Configurations (71)Digital Library
  41. Nextgov. 2021. Commercial Cloud Outages Are a Wake-Up Call. https://www.nextgov.com/ideas/2021/03/commercial-cloud-outages-are-wake-call/172731/ Accessed: 2023-11-01Google ScholarDiffy: Data-Driven Bug Finding for Configurations (73)
  42. David Oppenheimer, Archana Ganapathi, and David A Patterson. 2003. Why do Internet services fail, and what can be done about it? In 4th Usenix Symposium on Internet Technologies and Systems (USITS 03).Google ScholarDiffy: Data-Driven Bug Finding for Configurations (74)Digital Library
  43. Saswat Padhi. 2018. FlashProfileDemo: A C# application that demonstrates the capabilities of FlashProfile. https://github.com/SaswatPadhi/FlashProfileDemo/tree/master/tests Accessed: March 25, 2023Google ScholarDiffy: Data-Driven Bug Finding for Configurations (76)
  44. Saswat Padhi, Prateek Jain, Daniel Perelman, Oleksandr Polozov, Sumit Gulwani, and Todd D. Millstein. 2018. FlashProfile: A Framework for Synthesizing Data Profiles. PACMPL, 2, OOPSLA (2018), 150:1–150:28. https://doi.org/10.1145/3276520Google ScholarDiffy: Data-Driven Bug Finding for Configurations (77)Digital Library
  45. Oleksandr Polozov and Sumit Gulwani. 2015. FlashMeta: A Framework for Inductive Program Synthesis. SIGPLAN Not., 50, 10 (2015), oct, 107–126. issn:0362-1340 https://doi.org/10.1145/2858965.2814310Google ScholarDiffy: Data-Driven Bug Finding for Configurations (79)Digital Library
  46. Raymond Pompon. 2021. BGP, DNS, and the fragility of our critical systems. https://www.f5.com/labs/articles/cisotociso/bgp-dns-and-the-fragility-of-our-critical-systems Accessed: 2023-11-01Google ScholarDiffy: Data-Driven Bug Finding for Configurations (81)
  47. Ariel Rabkin and Randy Howard Katz. 2012. How hadoop clusters break. IEEE software, 30, 4 (2012), 88–94.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (82)
  48. Teri Radichel. 2023. About the 5-hour Microsoft Outage. https://medium.com/cloud-security/about-the-5-hour-microsoft-outage-18d47543769d Accessed: 2023-11-01Google ScholarDiffy: Data-Driven Bug Finding for Configurations (83)
  49. Yakov Rekhter, Susan Hares, and Tony Li. 2006. A Border Gateway Protocol 4 (BGP-4). RFC 4271. https://doi.org/10.17487/RFC4271Google ScholarDiffy: Data-Driven Bug Finding for Configurations (84)Digital Library
  50. Mark Santolucito, Ennan Zhai, Rahul Dhodapkar, Aaron Shim, and Ruzica Piskac. 2017. Synthesizing configuration file specifications with association rule learning. Proceedings of the ACM on Programming Languages, 1, OOPSLA (2017), 1–20.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (86)Digital Library
  51. Mark Santolucito, Ennan Zhai, and Ruzica Piskac. 2016. Probabilistic automated language learning for configuration files. In Computer Aided Verification: 28th International Conference, CAV 2016, Proceedings, Part II 28. Springer, Cham, Toronto, ON, Canada. 80–87.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (88)
  52. Temple F Smith and Michael S Waterman. 1981. Identification of common molecular subsequences. Journal of molecular biology, 147, 1 (1981), 195–197.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (89)Cross Ref
  53. Alan Tang, Siva Kesava Reddy Kakarla, Ryan Beckett, Ennan Zhai, Matt Brown, Todd Millstein, Yuval Tamir, and George Varghese. 2021. Campion: Debugging Router Configuration Differences. In Proceedings of the 2021 ACM SIGCOMM 2021 Conference (SIGCOMM ’21). Association for Computing Machinery, New York, NY, USA. 748–761. isbn:9781450383837 https://doi.org/10.1145/3452296.3472925Google ScholarDiffy: Data-Driven Bug Finding for Configurations (91)Digital Library
  54. Liam Tung. 2019. Azure global outage: Our DNS update mangled domain records, says Microsoft. https://www.zdnet.com/article/azure-global-outage-our-dns-update-mangled-domain-records-says-microsoft/ Accessed: 2023-11-01Google ScholarDiffy: Data-Driven Bug Finding for Configurations (93)
  55. Kurt Wise. 2017. High Number of AWS Misconfigurations Leaves Huge Security Holes. https://virtualizationreview.com/articles/2017/04/19/aws-misconfigurations-leaves-huge-security-holes.aspxGoogle ScholarDiffy: Data-Driven Bug Finding for Configurations (94)
  56. Tianyin Xu, Jiaqi Zhang, Peng Huang, Jing Zheng, Tianwei Sheng, Ding Yuan, Yuanyuan Zhou, and Shankar Pasupathy. 2013. Do not blame users for misconfigurations. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles. 244–259.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (95)Digital Library
  57. Hongkun Yang and Simon S. Lam. 2016. Real-time Verification of Network Properties Using Atomic Predicates. IEEE/ACM Trans. Netw., 24, 2 (2016), April, 887–900. issn:1063-6692 https://doi.org/10.1109/TNET.2015.2398197Google ScholarDiffy: Data-Driven Bug Finding for Configurations (97)Digital Library
  58. Zuoning Yin, Xiao Ma, Jing Zheng, Yuanyuan Zhou, Lakshmi N Bairavasundaram, and Shankar Pasupathy. 2011. An empirical study on configuration errors in commercial and open source systems. In Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles. 159–172.Google ScholarDiffy: Data-Driven Bug Finding for Configurations (99)Digital Library
  59. Iris Zarecki. 2019. 19 of the worst IT outages in 2019 – A Recap of Being Let Down. https://www.continuitysoftware.com/blog/19-of-the-worst-it-outages-in-2019-a-recap-of-being-let-down/Google ScholarDiffy: Data-Driven Bug Finding for Configurations (101)
  60. Jiaqi Zhang, Lakshminarayanan Renganarayana, Xiaolan Zhang, Niyu Ge, Vasanth Bala, Tianyin Xu, and Yuanyuan Zhou. 2014. EnCore: Exploiting System Environment and Correlation Information for Misconfiguration Detection. In Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’14). Association for Computing Machinery, New York, NY, USA. 687–700. isbn:9781450323055 https://doi.org/10.1145/2541940.2541983Google ScholarDiffy: Data-Driven Bug Finding for Configurations (102)Digital Library
  61. Peng Zhang, Xu Liu, Hongkun Yang, Ning Kang, Zhengchang Gu, and Hao Li. 2020. APKeep: Realtime Verification for Real Networks. In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20). USENIX Association, Santa Clara, CA. 241–255. isbn:978-1-939133-13-7 https://www.usenix.org/conference/nsdi20/presentation/zhang-pengGoogle ScholarDiffy: Data-Driven Bug Finding for Configurations (104)

Cited By

View all

Diffy: Data-Driven Bug Finding for Configurations (105)

    Index Terms

    1. Diffy: Data-Driven Bug Finding for Configurations

      1. Networks

        1. Network properties

          1. Network reliability

        2. Software and its engineering

          1. Software notations and tools

            1. Software configuration management and version control systems

        Recommendations

        • Implementation of Packet Filter Configurations Anomaly Detection System with SIERRA

          Information and Communications Security

          Abstract

          Packet filtering in a firewall is one of the useful tools for network security. Packet filtering examines network packet and decides whether to accept, or deny it and this decision is determined by a packet filtering configuration developed by the ...

          Read More

        • An Extensive Analysis of Efficient Bug Prediction Configurations

          PROMISE: Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering

          Background: Bug prediction helps developers steer maintenance activities towards the buggy parts of a software. There are many design aspects to a bug predictor, each of which has several options, i.e., software metrics, machine learning model, and ...

        • Effective Bug Triage Based on Historical Bug-Fix Information

          ISSRE '14: Proceedings of the 2014 IEEE 25th International Symposium on Software Reliability Engineering

          For complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been ...

          Read More

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        Get this Article

        • Information
        • Contributors
        • Published in

          Diffy: Data-Driven Bug Finding for Configurations (106)

          Proceedings of the ACM on Programming Languages Volume 8, Issue PLDI

          June 2024

          2198 pages

          EISSN:2475-1421

          DOI:10.1145/3554317

          • Editor:
          • Michael Hicks

            Amazon, USA

          Issue’s Table of Contents

          Copyright © 2024 Owner/Author

          This work is licensed under a Creative Commons Attribution International 4.0 License.

          Sponsors

            In-Cooperation

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 20 June 2024

              Published in pacmpl Volume 8, Issue PLDI

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Diffy: Data-Driven Bug Finding for Configurations (107)

              Author Tags

              • anomaly detection
              • configuration bug finding
              • template synthesis

              Qualifiers

              • research-article

              Conference

              Funding Sources

              • Diffy: Data-Driven Bug Finding for Configurations (109)

                Other Metrics

                View Article Metrics

              • Bibliometrics
              • Citations0
              • Article Metrics

                • Total Citations

                  View Citations
                • Total Downloads

                • Downloads (Last 12 months)0
                • Downloads (Last 6 weeks)0

                Other Metrics

                View Author Metrics

              • Cited By

                This publication has not been cited yet

              PDF Format

              View or Download as a PDF file.

              PDF

              eReader

              View online with eReader.

              eReader

              Digital Edition

              View this article in digital edition.

              View Digital Edition

              • Figures
              • Other

                Close Figure Viewer

                Browse AllReturn

                Caption

                View Issue’s Table of Contents

                Export Citations

                  Diffy: Data-Driven Bug Finding for Configurations (2024)
                  Top Articles
                  Latest Posts
                  Article information

                  Author: Terrell Hackett

                  Last Updated:

                  Views: 6253

                  Rating: 4.1 / 5 (72 voted)

                  Reviews: 95% of readers found this page helpful

                  Author information

                  Name: Terrell Hackett

                  Birthday: 1992-03-17

                  Address: Suite 453 459 Gibson Squares, East Adriane, AK 71925-5692

                  Phone: +21811810803470

                  Job: Chief Representative

                  Hobby: Board games, Rock climbing, Ghost hunting, Origami, Kabaddi, Mushroom hunting, Gaming

                  Introduction: My name is Terrell Hackett, I am a gleaming, brainy, courageous, helpful, healthy, cooperative, graceful person who loves writing and wants to share my knowledge and understanding with you.