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1. Gradual Soundness: Lessons from Static Python

   https://doi.org/10.22152/programming-journal.org/2023/7/2  

   Lu, Kuang-Chen; Greenman, Ben; Meyer, Carl; Viehland, Dino; Panse, Aniket; Krishnamurthi, Shriram (June 2022, The Art, Science, and Engineering of Programming)

   Context: Gradually-typed languages allow typed and untyped code to interoperate, but typically come with significant drawbacks. In some languages, the types are unreliable; in others, communication across type boundaries can be extremely expensive; and still others allow only limited forms of interoperability. The research community is actively seeking a sound, fast, and expressive approach to gradual typing. Inquiry: This paper describes Static Python, a language developed by engineers at Instagram that has proven itself sound, fast, and reasonably expressive in production. Static Python’s approach to gradual types is essentially a programmer-tunable combination of the concrete and transient approaches from the literature. Concrete types provide full soundness and low performance overhead, but impose nonlocal constraints. Transient types are sound in a shallow sense and easier to use; they help to bridge the gap between untyped code and typed concrete code. Approach: We evaluate the language in its current state and develop a model that captures the essence of its approach to gradual types. We draw upon personal communication, bug reports, and the Static Python regression test suite to develop this model. Knowledge: Our main finding is that the gradual soundness that arises from a mix of concrete and transient types is an effective way to lower the maintenance cost of the concrete approach. We also find that method-based JIT technology can eliminate the costs of the transient approach. On a more technical level, this paper describes two contributions: a model of Static Python and a performance evaluation of Static Python. The process of formalization found several errors in the implementation, including fatal errors. Grounding: Our model of Static Python is implemented in PLT Redex and tested using property-based soundness tests and 265 tests from the Static Python regression suite. This paper includes a small core of the model to convey the main ideas of the Static Python approach and its soundness. Our performance claims are based on production experience in the Instagram web server. Migrations to Static Python in the server have caused a 3.7\% increase in requests handled per second at maximum CPU load. Importance: Static Python is the first sound gradual language whose piece-meal application to a realistic codebase has consistently improved performance. Other language designers may wish to replicate its approach, especially those who currently maintain unsound gradual languages and are seeking a path to soundness. 

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2. Fast and Precise On-the-fly Patch Validation for All

   Chen, Lingchao; Ouyang, Yicheng; Zhang, Lingming (July 2021, IEEE/ACM International Conference on Software Engineering)

   null (Ed.)

   Generate-and-validate (G&V) automated program repair (APR) techniques have been extensively studied during the past decade. Meanwhile, such techniques can be extremely time-consuming due to the manipulation of program code to fabricate a large number of patches and also the repeated test executions on patches to identify potential fixes. PraPR, a recent G&V APR technique, reduces such costs by modifying program code directly at the level of compiled JVM bytecode with on-the-fly patch validation, which directly allows multiple bytecode patches to be tested within the same JVM process. However, PraPR is limited due to its unique bytecode-repair design, and is basically unsound/imprecise as it assumes that patch executions do not change global JVM state and affect later patch executions on the same JVM process. In this paper, we propose a unified patch validation framework, named UniAPR, to perform the first empirical study of on-the-fly patch validation for state-of-the-art source-code-level APR techniques widely studied in the literature; furthermore, UniAPR addresses the imprecise patch validation issue by resetting the JVM global state via runtime bytecode transformation. We have implemented UniAPR as a publicly available fully automated Maven Plugin. Our study demonstrates for the first time that on-the-fly patch validation can often speed up state-of-the-art source-code-level APR by over an order of magnitude, enabling all existing APR techniques to explore a larger search space to fix more bugs in the near future. Furthermore, our study shows the first empirical evidence that vanilla on-the-fly patch validation can be imprecise/unsound, while UniAPR with JVM reset is able to mitigate such issues with negligible overhead. 

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3. Logical bytecode reduction

   https://doi.org/10.1145/3453483.3454091  

   Kalhauge, Christian Gram; Palsberg, Jens (June 2021, 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI))

   null (Ed.)

   Reducing a failure-inducing input to a smaller one is challenging for input with internal dependencies because most sub-inputs are invalid. Kalhauge and Palsberg made progress on this problem by mapping the task to a reduction problem for dependency graphs that avoids invalid inputs entirely. Their tool J-Reduce efficiently reduces Java bytecode to 24 percent of its original size, which made it the most effective tool until now. However, the output from their tool is often too large to be helpful in a bug report. In this paper, we show that more fine-grained modeling of dependencies leads to much more reduction. Specifically, we use propositional logic for specifying dependencies and we show how this works for Java bytecode. Once we have a propositional formula that specifies all valid sub-inputs, we run an algorithm that finds a small, valid, failure-inducing input. Our algorithm interleaves runs of the buggy program and calls to a procedure that finds a minimal satisfying assignment. Our experiments show that we can reduce Java bytecode to 4.6 percent of its original size, which is 5.3 times better than the 24.3 percent achieved by J-Reduce. The much smaller output is more suitable for bug reports. 

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4. Design, implementation, and application of GPU-based Java bytecode interpreters

   https://doi.org/10.1145/3360603  

   Celik, Ahmet; Nie, Pengyu; Rossbach, Christopher_J; Gligoric, Milos (October 2019, Proceedings of the ACM on Programming Languages)

   We present the design and implementation of GVM, the first system for executing Java bytecode entirely on GPUs. GVM is ideal for applications that execute a large number of short-living tasks, which share a significant fraction of their codebase and have similar execution time. GVM uses novel algorithms, scheduling, and data layout techniques to adapt to the massively parallel programming and execution model of GPUs. We apply GVM to generate and execute tests for Java projects. First, we implement a sequence-based test generation on top of GVM and design novel algorithms to avoid redundant test sequences. Second, we use GVM to execute randomly generated test cases. We evaluate GVM by comparing it with two existing Java bytecode interpreters (Oracle JVM and Java Pathfinder), as well as with the Oracle JVM with just-in-time (JIT) compiler, which has been engineered and optimized for over twenty years. Our evaluation shows that sequence-based test generation on GVM outperforms both Java Pathfinder and Oracle JVM interpreter. Additionally, our results show that GVM performs as well as running our parallel sequence-based test generation algorithm using JVM with JIT with many CPU threads. Furthermore, our evaluation on several classes from open-source projects shows that executing randomly generated tests on GVM outperforms sequential execution on JVM interpreter and JVM with JIT. 

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5. Symbolic types for lenient symbolic execution

   https://doi.org/10.1145/3158128  

   Chang, Stephen; Knauth, Alex; Torlak, Emina (December 2017, Proceedings of the ACM on Programming Languages)

   We present lambda_sym, a typed λ-calculus forlenient symbolic execution, where some language constructs do not recognize symbolic values. Its type system, however, ensures safe behavior of all symbolic values in a program. Our calculus extends a base occurrence typing system with symbolic types and mutable state, making it a suitable model for both functional and imperative symbolically executed languages. Naively allowing mutation in this mixed setting introduces soundness issues, however, so we further addconcreteness polymorphism, which restores soundness without rejecting too many valid programs. To show that our calculus is a useful model for a real language, we implemented Typed Rosette, a typed extension of the solver-aided Rosette language. We evaluate Typed Rosette by porting a large code base, demonstrating that our type system accommodates a wide variety of symbolically executed programs. 

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