research-article

Public Access

Sound and efficient concurrency bug prediction

Authors:

Jens PalsbergAuthors Info & Claims

ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 255 - 267

https://doi.org/10.1145/3468264.3468549

Published: 18 August 2021 Publication History

Abstract

Concurrency bugs are extremely difficult to detect. Recently, several dynamic techniques achieve sound analysis. M2 is even complete for two threads. It is designed to decide whether two events can occur consecutively. However, real-world concurrency bugs can involve more events and threads. Some can occur when the order of two or more events can be exchanged even if they occur not consecutively. We propose a new technique SeqCheck to soundly decide whether a sequence of events can occur in a specified order. The ordered sequence represents a potential concurrency bug. And several known forms of concurrency bugs can be easily encoded into event sequences where each represents a way that the bug can occur. To achieve it, SeqCheck explicitly analyzes branch events and includes a set of efficient algorithms. We show that SeqCheck is sound; and it is also complete on traces of two threads.

We have implemented SeqCheck to detect three types of concurrency bugs and evaluated it on 51 Java benchmarks producing up to billions of events. Compared with M2 and other three recent sound race detectors, SeqCheck detected 333 races in ~30 minutes; while others detected from 130 to 285 races in ~6 to ~12 hours. SeqCheck detected 20 deadlocks in ~6 seconds. This is only one less than Dirk; but Dirk spent more than one hour. SeqCheck also detected 30 atomicity violations in ~20 minutes. The evaluation shows SeqCheck can significantly outperform existing concurrency bug detectors.

References

[1]

Swarnendu Biswas, Man Cao, Minjia Zhang, Michael D. Bond, and Benjamin P. Wood. 2017. Lightweight data race detection for production runs. In Proceedings of the 26th International Conference on Compiler Construction (CC’17). Association for Computing Machinery, ustin, TX, USA. 11–21. isbn:9781450352338 https://doi.org/10.1145/3033019.3033020

Digital Library

[2]

Swarnendu Biswas, Minjia Zhang, Michael D. Bond, and Brandon Lucia. 2015. Valor: efficient, software-only region conflict exceptions. ACM SIGPLAN Notices, 50, 10 (2015), Oct., 241–259. issn:9781450336895 https://doi.org/10.1145/2858965.2814292

Digital Library

[3]

S. M. Blackburn, R. Garner, C. Hoffman, A. M. Khan, K. S. McKinley, R. Bentzur, A. Diwan, D. Feinberg, D. Frampton, S. Z. Guyer, M. Hirzel, A. Hosking, M. Jump, H. Lee, J. E. B. Moss, A. Phansalkar, D. Stefanović, T. VanDrunen, D. von Dincklage, and B. Wiedermann. 2006. The DaCapo Benchmarks: Java Benchmarking Development and Analysis. In OOPSLA ’06: Proceedings of the 21st annual ACM SIGPLAN conference on Object-Oriented Programing, Systems, Languages, and Applications. ACM Press, New York, NY, USA. 169–190. https://doi.org/10.1145/1167473.1167488

Digital Library

[4]

Michael D. Bond, Katherine E. Coons, and Kathryn S. McKinley. 2010. PACER: Proportional Detection of Data Races. In Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’10). Association for Computing Machinery, New York, NY, USA. 255–268. isbn:9781450300193 https://doi.org/10.1145/1806596.1806626

Digital Library

[5]

Yan Cai and Lingwei Cao. 2015. Effective and Precise Dynamic Detection of Hidden Races for Java Programs. ESEC/FSE 2015. Association for Computing Machinery, New York, NY, USA. 450–461. isbn:9781450336758 https://doi.org/10.1145/2786805.2786839

Digital Library

[6]

Yan Cai and W. K. Chan. 2012. MagicFuzzer: Scalable Deadlock Detection for Large-scale Applications. In Proceedings of the 34th International Conference on Software Engineering (ICSE ’12). IEEE Press, Piscataway, NJ, USA. 606–616. isbn:978-1-4673-1067-3 http://dl.acm.org/citation.cfm?id=2337223.2337294

[7]

Yan Cai, Jian Zhang, Lingwei Cao, and Jian Liu. 2016. A Deployable Sampling Strategy for Data Race Detection. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2016). Association for Computing Machinery, New York, NY, USA. 810–821. isbn:9781450342186 https://doi.org/10.1145/2950290.2950310

Digital Library

[8]

Yan Cai, Biyun Zhu, Ruijie Meng, Hao Yun, Liang He, Purui Su, and Bin Liang. 2019. Detecting Concurrency Memory Corruption Vulnerabilities. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2019). Association for Computing Machinery, New York, NY, USA. 706–717. isbn:9781450355728 https://doi.org/10.1145/3338906.3338927

Digital Library

[9]

Jong-Deok Choi, Keunwoo Lee, and Alexey Loginov. 2002. Efficient and precise datarace detection for multithreaded object-oriented programs. ACM Sigplan Notices, 37, 5 (2002), June, 258–269. https://doi.org/10.1145/543552.512560

Digital Library

[10]

Intel Corporation. 2016. Intel Inspector. https://software.intel.com/en-us/intel-inspector-xe

[11]

Anne Dinning and Edith Schonberg. 1991. Detecting access anomalies in programs with critical sections. ACM SIGPLAN Notices, 26, 12 (1991), Dec., 85–96. https://doi.org/10.1145/127695.122767

Digital Library

[12]

Laura Effinger-Dean, Brandon lucia, luis Ceze, Dan Grossman, and Hans-J Boehm. 2012. IFRit: interference-free regions for dynamic data-race detection. In Acm International Conference on Object Oriented Programming Systems Languages & Applications (OOPSLA ’12). isbn:9781450315616 https://doi.org/10.1145/2398857.2384650

Digital Library

[13]

Tayfun Elmas, Shaz Qadeer, and Serdar Tasiran. 2007. Goldilocks: a race and transaction-aware java runtime. Acm Sigplan Notices, 42, 6 (2007), June, 245–255. https://doi.org/10.1145/1273442.1250762

Digital Library

[14]

Dawson Engler and Ken Ashcraft. 2003. RacerX : Effective, static detection of race conditions and deadlocks. ACM SIGOPS Operating Systems Review, 37, 5 (2003), Oct., 237–252. issn:1581137575 https://doi.org/10.1145/945445.945468

Digital Library

[15]

John Erickson, Madanlal Musuvathi, Sebastian Burckhardt, and Kirk Olynyk. 2010. Effective data-race detection for the kernel. In Proceedings of the 9th USENIX conference on Operating systems design and implementation (OSDI ’10). 151–162. https://doi.org/10.5555/1924943.1924954

Digital Library

[16]

Azadeh Farzan, P. Madhusudan, Niloofar Razavi, and Francesco Sorrentino. 2012. Predicting Null-Pointer Dereferences in Concurrent Programs. In Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering (FSE ’12). Association for Computing Machinery, New York, NY, USA. Article 47, 11 pages. isbn:9781450316149 https://doi.org/10.1145/2393596.2393651

Digital Library

[17]

Mingdong Feng and Charles E. Leiserson. 1997. Efficient detection of determinacy races in Cilk programs. In Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures (SPAA ’97). 1–11.

[18]

Peter M. Fenwick. 1994. A New Data Structure for Cumulative Frequency Tables. Softw. Pract. Exper., 24, 3 (1994), March, 327–336. issn:0038-0644 https://doi.org/10.1002/spe.4380240306

Digital Library

[19]

Cormac Flanagan and Stephen Freund. 2010. The RoadRunner Dynamic Analysis Framework for Concurrent Programs. In Proceedings of the 9th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering (PASTE ’10). 1–8.

Digital Library

[20]

Cormac Flanagan and Stephen N. Freund. 2009. FastTrack: Efficient and Precise Dynamic Race Detection. In Proceedings of the 30th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’09). ACM, New York, NY, USA. 121–133. isbn:978-1-60558-392-1 https://doi.org/10.1145/1542476.1542490

Digital Library

[21]

Kaan Genç, Jake Roemer, Yufan Xu, and Michael D. Bond. 2019. Dependence-Aware, Unbounded Sound Predictive Race Detection. Proc. ACM Program. Lang., 3, OOPSLA (2019), Article 179, Oct., 30 pages. https://doi.org/10.1145/3360605

Digital Library

[22]

Yu Guo, Yan Cai, and Zijiang Yang. 2017. AtexRace: Across Thread and Execution Sampling for in-House Race Detection. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2017). Association for Computing Machinery, New York, NY, USA. 315–325. isbn:9781450351058 https://doi.org/10.1145/3106237.3106242

Digital Library

[23]

Jeff Huang. 2018. UFO: Predictive Concurrency Use-after-Free Detection. In Proceedings of the 40th International Conference on Software Engineering (ICSE ’18). Association for Computing Machinery, New York, NY, USA. 609–619. isbn:9781450356381 https://doi.org/10.1145/3180155.3180225

Digital Library

[24]

Jeff Huang, Patrick O’Neil Meredith, and Grigore Rosu. 2014. Maximal Sound Predictive Race Detection with Control Flow Abstraction. In Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’14). Association for Computing Machinery, New York, NY, USA. 337–348. isbn:9781450327848 https://doi.org/10.1145/2594291.2594315

Digital Library

[25]

Joab Jackson. 2012. Nasdaq’s Facebook glitch came from ‘race conditions’. http://www.computerworld.com/s/article/9227350

[26]

Pallavi Joshi, Chang-Seo Park, Koushik Sen, and Mayur Naik. 2009. A Randomized Dynamic Program Analysis Technique for Detecting Real Deadlocks. In Proceedings of the 30th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’09). Association for Computing Machinery, New York, NY, USA. 110–120. isbn:9781605583921 https://doi.org/10.1145/1542476.1542489

Digital Library

[27]

Horatiu Jula, Daniel Tralamazza, Cristian Zamfir, and George Candea. 2008. Deadlock Immunity: Enabling Systems to Defend against Deadlocks. In Proceedings of the 8th USENIX Conference on Operating Systems Design and Implementation (OSDI’08). USENIX Association, USA. 295–308.

Digital Library

[28]

Christian Gram Kalhauge and Jens Palsberg. 2018. Sound Deadlock Prediction. Proc. ACM Program. Lang., 2, OOPSLA (2018), Article 146, Oct., 29 pages. https://doi.org/10.1145/3276516

Digital Library

[29]

Dileep Kini, Umang Mathur, and Mahesh Viswanathan. 2017. Dynamic Race Prediction in Linear Time. PLDI 2017. Association for Computing Machinery, New York, NY, USA. 157–170. isbn:9781450349888 https://doi.org/10.1145/3062341.3062374

Digital Library

[30]

Lamport. 1979. How to Make a Multiprocessor Computer That Correctly Executes Multiprocess Programs. IEEE Trans. Comput., C-28, 9 (1979), 690–691. https://doi.org/10.1109/TC.1979.1675439

Digital Library

[31]

Leslie Lamport. 1978. Time, Clocks, and the Ordering of Events in a Distributed System. Commun. ACM, 21, 7 (1978), July, 558–565. issn:0001-0782 https://doi.org/10.1145/359545.359563

Digital Library

[32]

N. G. Leveson and C. S. Turner. 1993. An investigation of the Therac-25 accidents. Computer, 26, 7 (1993), 18–41. https://doi.org/10.1109/MC.1993.274940

Digital Library

[33]

Shan Lu, Soyeon Park, Eunsoo Seo, and Yuanyuan Zhou. 2008. Learning from Mistakes: A Comprehensive Study on Real World Concurrency Bug Characteristics. In Proceedings of the 13th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS XIII). Association for Computing Machinery, New York, NY, USA. 329–339. isbn:9781595939586 https://doi.org/10.1145/1346281.1346323

Digital Library

[34]

Brandon Lucia, Joseph Devietti, Karin Strauss, and Luis Ceze. 2008. Atom-Aid: Detecting and Surviving Atomicity Violations. In Proceedings of the 35th Annual International Symposium on Computer Architecture (ISCA ’08). IEEE Computer Society, USA. 277–288. isbn:9780769531748 https://doi.org/10.1109/ISCA.2008.4

Digital Library

[35]

Umang Mathur. 2020. RAPID : Dynamic Analysis for Concurrent Programs. https://github.com/umangm/rapid

[36]

Umang Mathur, Dileep Kini, and Mahesh Viswanathan. 2018. What Happens-after the First Race? Enhancing the Predictive Power of Happens-before Based Dynamic Race Detection. 2, OOPSLA (2018), Article 145, Oct., 29 pages. https://doi.org/10.1145/3276515

Digital Library

[37]

Umang Mathur, Andreas Pavlogiannis, and Mahesh Viswanathan. 2021. Optimal Prediction of Synchronization-Preserving Races. Proc. ACM Program. Lang., 5, POPL (2021), Article 36, Jan., 29 pages. https://doi.org/10.1145/3434317

Digital Library

[38]

Mayur Naik, Alex Aiken, and John Whaley. 2006. Effective static race detection for Java. ACM SIGPLAN Notices, 41, 6 (2006), June, 308–319. issn:1595933204 https://doi.org/10.1145/1133255.1134018

Digital Library

[39]

Hiroyasu Nishiyama. 2004. Detecting Data Races Using Dynamic Escape Analysis Based on Read Barrier. In Proceedings of the 3rd conference on Virtual Machine Research And Technology Symposium (VM ’04). New York, NY, USA. 127–138. https://doi.org/10.5555/1267242.1267252

Digital Library

[40]

Robert O’Callahan and Jong-Deok Choi. 2003. Hybrid dynamic data race detection. In Proceedings of the ninth ACM SIGPLAN symposium on Principles and practice of parallel programming (PPoPP’03). New York, NY, USA. 167–178. isbn:1581135882 https://doi.org/10.1145/966049.781528

Digital Library

[41]

Chang-Seo Park and Koushik Sen. 2008. Randomized Active Atomicity Violation Detection in Concurrent Programs. In Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering (SIGSOFT ’08/FSE-16). Association for Computing Machinery, New York, NY, USA. 135–145. isbn:9781595939951 https://doi.org/10.1145/1453101.1453121

Digital Library

[42]

Chang-Seo Park and Koushik Sen. 2008. Randomized Active Atomicity Violation Detection in Concurrent Programs. In Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering (SIGSOFT ’08/FSE-16). Association for Computing Machinery, New York, NY, USA. 135–145. isbn:9781595939951 https://doi.org/10.1145/1453101.1453121

Digital Library

[43]

Andreas Pavlogiannis. 2020. Fast, sound, and effectively complete dynamic race prediction. Proc. ACM Program. Lang., 4, POPL (2020), 17:1–17:29. https://doi.org/10.1145/3371085

Digital Library

[44]

Kevin Poulsen. 2012. Software bug contributed to blackout. Security Focus. http://www.securityfocus.com/news/8016

[45]

Eli Pozniansky and Assaf Schuster. 2007. Multirace: Efficient On-the-fly Data Race Detection In Multithreaded C++ Programs. ACM Trans. Comput. Syst., 19, 3 (2007), Nov., 327–340. https://doi.org/10.1002/cpe.1064

[46]

Polyvios Pratikakis, Jeffrey S. Foster, and Michael Hicks. 2011. LOCKSMITH: Practical static race detection for C. ACM Transactions on Programming Languages and Systems, 33, 1 (2011), Jan., https://doi.org/10.1145/1889997.1890000

Digital Library

[47]

Christoph von Praun and Thomas R. Gross. 2001. Object Race Detection. ACM Sigplan Notices, 36, 11 (2001), Nov., 70–82. https://doi.org/10.1145/504311.504288

Digital Library

[48]

Cosmin Radoi and Danny Dig. 2013. Practical static race detection for Java parallel loops. In Proceedings of the 2013 International Symposium on Software Testing and Analysis (ISSTA 2013). 178–190.

Digital Library

[49]

Raghavan Raman, Jisheng Zhao, Vivek Sarkar, Martin Vechev, and Eran Yahav. 2012. Scalable and precise dynamic datarace detection for structured parallelism. ACM SIGPLAN Notices, 47, 6 (2012), June, 531–542. issn:9781450312059 https://doi.org/10.1145/2345156.2254127

Digital Library

[50]

Jake Roemer, Kaan Genc, and Michael D. Bond. 2018. High-Coverage, Unbounded Sound Predictive Race Detection. PLDI 2018. 374–389. isbn:9781450356985 https://doi.org/10.1145/3192366.3192385

Digital Library

[51]

Stefan Savage, Michael Burrows, Greg Nelson, Patrick Sobalvarro, and Thomas Anderson. 1997. Eraser: A Dynamic Data Race Detector for Multithreaded Programs. ACM Trans. Comput. Syst., 15, 4 (1997), Nov., 391–411. issn:0734-2071 https://doi.org/10.1145/265924.265927

Digital Library

[52]

Koushik Sen. 2008. Race directed random testing of concurrent programs. In ACM SIGPLAN Notices (PLDI ’08). 11–21. isbn:9781595938602 https://doi.org/10.1145/1379022.1375584

Digital Library

[53]

Konstantin Serebryany and Timur Iskhodzhanov. 2009. ThreadSanitizer: data race detection in practice. In Proceedings of the Workshop on Binary Instrumentation and Applications (WBIA ’09). 62–71. isbn:9781605587936 https://doi.org/10.1145/1791194.1791203

Digital Library

[54]

Konstantin Serebryany, Alexander Potapenko, Timur Iskhodzhanov, and Dmitriy Vyukov. 2012. Dynamic Race Detection with LLVM Compiler. In Runtime Verification (RV 2011). 110–114. isbn:9783642298592 https://doi.org/10.1007/978-3-642-29860-8_9

Digital Library

[55]

Yannis Smaragdakis, Jacob Evans, Caitlin Sadowski, Jaeheon Yi, and Cormac Flanagan. 2012. Sound Predictive Race Detection in Polynomial Time. In Proceedings of the 39th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL ’12). Association for Computing Machinery, New York, NY, USA. 387–400. isbn:9781450310833 https://doi.org/10.1145/2103656.2103702

Digital Library

[56]

Francesco Sorrentino, Azadeh Farzan, and P. Madhusudan. 2010. PENELOPE: Weaving Threads to Expose Atomicity Violations. In Proceedings of the Eighteenth ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE ’10). Association for Computing Machinery, New York, NY, USA. 37–46. isbn:9781605587912 https://doi.org/10.1145/1882291.1882300

Digital Library

[57]

Kaushik Veeraraghavan, Peter M. Chen, Jason Flinn, and Satish Narayanasamy. 2011. Detecting and surviving data races using complementary schedules. In Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles (SOSP ’11). 369–384. https://doi.org/10.1145/2043556.2043590

Digital Library

[58]

Jan Wen Voung, Ranjit Jhala, and Sorin Lerner. 2007. RELAY: static race detection on millions of lines of code. In Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering (ESEC-FSE ’07). 205–214.

Digital Library

[59]

Adarsh Yoga, Santosh Nagarakatte, and Aarti Gupta. 2016. Parallel data race detection for task parallel programs with locks. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2016). 833–845.

Digital Library

[60]

Yuan Yu, Tom Rodeheffer, and Wei Chen. 2005. RaceTrack: Efficient Detection of Data Race Conditions via Adaptive Tracking. In Proceedings of the Twentieth ACM Symposium on Operating Systems Principles (SOSP ’05). Association for Computing Machinery, New York, NY, USA. 221–234. isbn:1595930795 https://doi.org/10.1145/1095810.1095832

Digital Library

[61]

Sheng Zhan and Jeff Huang. 2016. ECHO: instantaneous in situ race detection in the IDE. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2016). 775–786.

Digital Library

Cited By

Ang ZMathur U(2024)Predictive Monitoring against Pattern Regular LanguagesProceedings of the ACM on Programming Languages10.1145/36329158:POPL(2191-2225)Online publication date: 5-Jan-2024
https://dl.acm.org/doi/10.1145/3632915
Tunç HDeshmukh ACirisci BEnea CPavlogiannis ATsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)CSSTs: A Dynamic Data Structure for Partial Orders in Concurrent Execution AnalysisProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 310.1145/3620666.3651358(223-238)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620666.3651358
Guo XQi XLi YWu C(2024)PredRacer: Predictively Detecting Data Races in Android Applications2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER60148.2024.00031(239-249)Online publication date: 12-Mar-2024
https://doi.org/10.1109/SANER60148.2024.00031
Show More Cited By

Index Terms

Sound and efficient concurrency bug prediction
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software organization and properties
    1. Contextual software domains
      1. Operating systems
        Process management
        Multithreading
        Scheduling

Recommendations

Scrider: Using Single Critical Sections to Avoid Deadlocks
IMCCC '14: Proceedings of the 2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control

We propose a method, called Scrider, to avoid dead-locks before they manifest themselves. When running it togeth-er with a multithreaded program, Scrider interferes in thread scheduling to ensure that at any time there is only one thread in the critical ...
Adaptively generating high quality fixes for atomicity violations
ESEC/FSE 2017: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering

It is difficult to fix atomicity violations correctly. Existing gate lock algorithm (GLA) simply inserts gate locks to serialize exe-cutions, which may introduce performance bugs and deadlocks. Synthesized context-aware gate locks (by Grail) require ...
Efficient data race and deadlock prevention in concurrent object-oriented programs
OOPSLA '04: Companion to the 19th annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications

The main goal of this PhD thesis is to propose and implement a methodology for the construction of programs based on the SCOOP model, and for modular reasoning about their correctness and liveness properties. In particular, the set of correctness rules ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 2021

1690 pages

ISBN:9781450385626

DOI:10.1145/3468264

General Chairs:
Diomidis Spinellis
Athens University of Economics and Business, Greece
,
Georgios Gousios
Facebook, Netherlands / Delft University of Technology, Netherlands
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Massimiliano Di Penta
University of Sannio, Italy

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 August 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Artifacts Evaluated & Reusable / v1.1

Author Tags

Qualifiers

Research-article

Funding Sources

National Natural Science Foundation of China
National Science Foundation
Youth Innovation Promotion Association of the Chinese Academy of Sciences
Key Research Program of Frontier Sciences, CAS

Conference

ESEC/FSE '21

Sponsor:

SIGSOFT

ESEC/FSE '21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 23 - 28, 2021

Athens, Greece

Acceptance Rates

Overall Acceptance Rate 112 of 543 submissions, 21%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

21
Total Citations
View Citations
1,121
Total Downloads

Downloads (Last 12 months)289
Downloads (Last 6 weeks)43

Reflects downloads up to 14 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ang ZMathur U(2024)Predictive Monitoring against Pattern Regular LanguagesProceedings of the ACM on Programming Languages10.1145/36329158:POPL(2191-2225)Online publication date: 5-Jan-2024
https://dl.acm.org/doi/10.1145/3632915
Tunç HDeshmukh ACirisci BEnea CPavlogiannis ATsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)CSSTs: A Dynamic Data Structure for Partial Orders in Concurrent Execution AnalysisProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 310.1145/3620666.3651358(223-238)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620666.3651358
Guo XQi XLi YWu C(2024)PredRacer: Predictively Detecting Data Races in Android Applications2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER60148.2024.00031(239-249)Online publication date: 12-Mar-2024
https://doi.org/10.1109/SANER60148.2024.00031
Ren HLi YChen LZhou YNie C(2024)Why and how bug blocking relations are breakableInformation and Software Technology10.1016/j.infsof.2023.107354166:COnline publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.infsof.2023.107354
Ang ZMathur U(2024)Predictive Monitoring with Strong Trace PrefixesComputer Aided Verification10.1007/978-3-031-65630-9_9(182-204)Online publication date: 25-Jul-2024
https://doi.org/10.1007/978-3-031-65630-9_9
Dai YWu PCui SMa L(2024)Intrathread Method Orders Based Adaptive Testing of Concurrent ObjectsTheoretical Aspects of Software Engineering10.1007/978-3-031-64626-3_6(91-108)Online publication date: 14-Jul-2024
https://doi.org/10.1007/978-3-031-64626-3_6
Ren HMa MZhang XCao YNie C(2023)Effective Recommendation of Cross-Project Correlated Issues based on Issue MetricsProceedings of the 14th Asia-Pacific Symposium on Internetware10.1145/3609437.3609462(1-1)Online publication date: 4-Aug-2023
https://dl.acm.org/doi/10.1145/3609437.3609462
Sun CDai HGeng NLiu HChen TWu PCai YWang J(2023)An Interleaving Guided Metamorphic Testing Approach for Concurrent ProgramsACM Transactions on Software Engineering and Methodology10.1145/360718233:1(1-21)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3607182
Tunç HMathur UPavlogiannis AViswanathan M(2023)Sound Dynamic Deadlock Prediction in Linear TimeProceedings of the ACM on Programming Languages10.1145/35912917:PLDI(1733-1758)Online publication date: 6-Jun-2023
https://dl.acm.org/doi/10.1145/3591291
Ma XAshraf IChan W(2023)Davida: A Decentralization Approach to Localizing Transaction Sequences for Debugging Transactional Atomicity ViolationsIEEE Transactions on Reliability10.1109/TR.2022.317668072:2(808-826)Online publication date: Jun-2023
https://doi.org/10.1109/TR.2022.3176680
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

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

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents