Runtime predictive analysis

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In computer science, Runtime predictive analysis or (predictive analysis) is a runtime verification technique for detecting property violations in program executions inferred from an observed execution. An important class of predictive analysis methods has been developed for detecting concurrency errors (such as data races) in concurrent programs, where a runtime monitor is used to predict errors which did not happen in the observed run, but can happen in an alternative execution of the same program.


Informally, given an execution t, predictive analysis checks errors in a reordered trace t' of t. t' is called feasible from t if any program that can generate t can also generate t'.

In the context of concurrent programs, a predictive technique is sound if it only predicts concurrency errors in feasible executions of the causal model of the observed trace. Assuming the analysis has no knowledge about the source code of the program, the analysis is complete (also called maximal[1][2]) if the inferred class of executions contains all executions that have the same program order and communication order prefix of the observed trace.


Predictive analysis has been applied to detect a wide class of concurrency errors, including:

  • Data races
  • Deadlocks[3][4]
  • Atomicity violations [5]
  • Order violations, e.g., use-after-free errors[6]


As is typical with dynamic program analysis, predictive analysis first instruments the source program. At runtime, the analysis can be performed online, in order to detect errors on the fly. Alternatively, the instrumentation can simply dump the execution trace for offline analysis. The latter approach is preferred for expensive refined predictive analyses that require random access to the execution trace or take more than linear time.

Incorporating data and control-flow analysis

Static analysis can be first conducted to gather data and control-flow dependence information about the source program, which can help construct the causal model during online executions. This allows predictive analysis to infer a larger class of executions based on the observed execution. Intuitively, a feasible reordering can change the last writer of a memory read (data dependence) if the read, in turn, cannot affect whether any accesses execute (control dependence).[7][8]


Partial order based techniques

Partial order based techniques are most often employed for online race detection. At runtime, a partial order over the events in the trace is constructed, and any unordered pairs of critical events are reported as races. Many predictive techniques for race detection are based on the happens-before relation or a weakened version of it. Such techniques can typically be implemented efficiently with vector clock algorithms, allowing only one pass of the whole input trace as it is being generated, and are thus suitable for online deployment. [9] [10] [11]

SMT-based techniques

SMT encodings allow the analysis to extract a refined causal model from an execution trace, as a (possibly very large) mathematical formula. Furthermore, control-flow information can be incorporated into the model. SMT-based techniques can achieve soundness and completeness (also called maximal causality[2] [1]), but has exponential-time complexity with respect to the trace size. In practice, the analysis is typically deployed to bounded segments of an execution trace, thus trading completeness for scalability. [7] [12] [13] [14]

Other techniques

In the context of data race detection, sound polynomial-time predictive analyses have been developed, with good, close to maximal predictive capability. [15]


Here is a partial list of tools that use predictive analyses to detect concurrency errors, sorted alphabetically.

  • Rapid[16] : a lightweight framework for implementing dynamic race detection engines.
  • RoadRunner[17] : a dynamic analysis framework designed to facilitate rapid prototyping and experimentation with dynamic analyses for concurrent Java programs.
  • RV-Predict[18] : SMT-based predictive race detection.
  • UFO[19] : SMT-based predictive use-after-free detection.


  1. 1.0 1.1 Şerbănuţă, Traian Florin; Chen, Feng; Roşu, Grigore (2013). "Maximal Causal Models for Sequentially Consistent Systems". 7687: 136–150. doi:10.1007/978-3-642-35632-2_16. ISSN 0302-9743. {{cite journal}}: Cite journal requires |journal= (help)
  2. 2.0 2.1 Huang, Jeff (2015). "Stateless model checking concurrent programs with maximal causality reduction": 165–174. doi:10.1145/2737924.2737975. {{cite journal}}: Cite journal requires |journal= (help)
  3. Kalhauge, Christian Gram; Palsberg, Jens (2018). "Sound deadlock prediction". Proceedings of the ACM on Programming Languages. 2 (OOPSLA): 1–29. doi:10.1145/3276516. ISSN 2475-1421.
  4. "Sound Dynamic Deadlock Prediction in Linear Time" (PDF).
  5. "Atomicity Checking in Linear Time using Vector Clocks" (PDF).
  6. Huang, Jeff (2018). "UFO": 609–619. doi:10.1145/3180155.3180225. {{cite journal}}: Cite journal requires |journal= (help)
  7. 7.0 7.1 Huang, Jeff; Meredith, Patrick O'Neil; Rosu, Grigore (2013). "Maximal sound predictive race detection with control flow abstraction": 337–348. doi:10.1145/2594291.2594315. {{cite journal}}: Cite journal requires |journal= (help)
  8. Genç, Kaan; Roemer, Jake; Xu, Yufan; Bond, Michael D. (2019). "Dependence-aware, unbounded sound predictive race detection". Proceedings of the ACM on Programming Languages. 3 (OOPSLA): 1–30. doi:10.1145/3360605. ISSN 2475-1421.
  9. Smaragdakis, Yannis; Evans, Jacob; Sadowski, Caitlin; Yi, Jaeheon; Flanagan, Cormac (2012). "Sound predictive race detection in polynomial time". ACM SIGPLAN Notices. 47 (1): 387. doi:10.1145/2103621.2103702. ISSN 0362-1340.
  10. Kini, Dileep; Mathur, Umang; Viswanathan, Mahesh (2017). "Dynamic race prediction in linear time": 157–170. doi:10.1145/3062341.3062374. {{cite journal}}: Cite journal requires |journal= (help)
  11. Roemer, Jake; Genç, Kaan; Bond, Michael D. (2018). "High-coverage, unbounded sound predictive race detection": 374–389. doi:10.1145/3192366.3192385. {{cite journal}}: Cite journal requires |journal= (help)
  12. Liu, Peng; Tripp, Omer; Zhang, Xiangyu (2016). "IPA: improving predictive analysis with pointer analysis": 59–69. doi:10.1145/2931037.2931046. {{cite journal}}: Cite journal requires |journal= (help)
  13. Wang, Chao; Kundu, Sudipta; Ganai, Malay; Gupta, Aarti (2009). "Symbolic Predictive Analysis for Concurrent Programs". 5850: 256–272. doi:10.1007/978-3-642-05089-3_17. ISSN 0302-9743. {{cite journal}}: Cite journal requires |journal= (help)
  14. Said, Mahmoud; Wang, Chao; Yang, Zijiang; Sakallah, Karem (2011). "Generating Data Race Witnesses by an SMT-Based Analysis". 6617: 313–327. doi:10.1007/978-3-642-20398-5_23. ISSN 0302-9743. {{cite journal}}: Cite journal requires |journal= (help)
  15. Pavlogiannis, Andreas (2020). "Fast, sound, and effectively complete dynamic race prediction". Proceedings of the ACM on Programming Languages. 4 (POPL): 1–29. doi:10.1145/3371085. ISSN 2475-1421.
  16. Mathur, Umang (2020-02-16), umangm/rapid, retrieved 2020-02-28
  17. stephenfreund (2020-02-06), stephenfreund/RoadRunner, retrieved 2020-02-28
  18. "Runtime Verification Inc. | RV-Predict". Retrieved 2020-02-28.
  19. parasol-aser (2020-02-18), parasol-aser/UFO, retrieved 2020-02-28

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