Octave Larose, Sophie Kaleba, Humphrey Burchell, Stefan Marr
Thanks to partial evaluation and meta-tracing, it became practical to build language implementations that reach state-of-the-art peak performance by implementing only an interpreter. Systems such as RPython and GraalVM provide components such as a garbage collector and just-in-time compiler in a language-agnostic manner, greatly reducing implementation effort. However, meta-compilation-based language implementations still need to improve further to reach the low memory use and fast warmup behavior that custom-built systems provide. A key element in this endeavor is interpreter performance. Folklore tells us that bytecode interpreters are superior to abstract-syntax-tree (AST) interpreters both in terms of memory use and run-time performance. This work assesses the trade-offs between AST and bytecode interpreters to verify common assumptions and whether they hold in the context of meta-compilation systems. We implemented four interpreters, each an AST and a bytecode one using RPython and GraalVM. We keep the difference between the interpreters as small as feasible to be able to evaluate interpreter performance, peak performance, warmup, memory use, and the impact of individual optimizations. Our results show that both systems indeed reach performance close to Node.js/V8. Looking at interpreter-only performance, our AST interpreters are on par with, or even slightly faster than their bytecode counterparts. After just-in-time compilation, the results are roughly on par. This means bytecode interpreters do not have their widely assumed performance advantage. However, we can confirm that bytecodes are more compact in memory than ASTs, which becomes relevant for larger applications. However, for smaller applications, we noticed that bytecode interpreters allocate more memory because boxing avoidance is not as applicable, and because the bytecode interpreter structure requires memory, e.g., for a reified stack. Our results show AST interpreters to be competitive on top of meta-compilation systems. Together with possible engineering benefits, they should thus not be discounted so easily in favor of bytecode interpreters.
{"title":"AST vs. Bytecode: Interpreters in the Age of Meta-Compilation","authors":"Octave Larose, Sophie Kaleba, Humphrey Burchell, Stefan Marr","doi":"10.1145/3622808","DOIUrl":"https://doi.org/10.1145/3622808","url":null,"abstract":"Thanks to partial evaluation and meta-tracing, it became practical to build language implementations that reach state-of-the-art peak performance by implementing only an interpreter. Systems such as RPython and GraalVM provide components such as a garbage collector and just-in-time compiler in a language-agnostic manner, greatly reducing implementation effort. However, meta-compilation-based language implementations still need to improve further to reach the low memory use and fast warmup behavior that custom-built systems provide. A key element in this endeavor is interpreter performance. Folklore tells us that bytecode interpreters are superior to abstract-syntax-tree (AST) interpreters both in terms of memory use and run-time performance. This work assesses the trade-offs between AST and bytecode interpreters to verify common assumptions and whether they hold in the context of meta-compilation systems. We implemented four interpreters, each an AST and a bytecode one using RPython and GraalVM. We keep the difference between the interpreters as small as feasible to be able to evaluate interpreter performance, peak performance, warmup, memory use, and the impact of individual optimizations. Our results show that both systems indeed reach performance close to Node.js/V8. Looking at interpreter-only performance, our AST interpreters are on par with, or even slightly faster than their bytecode counterparts. After just-in-time compilation, the results are roughly on par. This means bytecode interpreters do not have their widely assumed performance advantage. However, we can confirm that bytecodes are more compact in memory than ASTs, which becomes relevant for larger applications. However, for smaller applications, we noticed that bytecode interpreters allocate more memory because boxing avoidance is not as applicable, and because the bytecode interpreter structure requires memory, e.g., for a reified stack. Our results show AST interpreters to be competitive on top of meta-compilation systems. Together with possible engineering benefits, they should thus not be discounted so easily in favor of bytecode interpreters.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
After decades of research, constructing call graphs for modern C-based software remains either imprecise or inefficient when scaling up to the ever-growing complexity. The main culprit is the difficulty of resolving function pointers, as precise pointer analyses are cubic in nature and become exponential when considering calling contexts. This paper takes a practical stance by first conducting a comprehensive empirical study of function pointer manipulations in the wild. By investigating 5355 indirect calls in five popular open-source systems, we conclude that, instead of the past uniform treatments for function pointers, a cocktail approach can be more effective in “squeezing” the number of difficult pointers to a minimum using a potpourri of cheap methods. In particular, we decompose the costs of constructing highly precise call graphs of big code by tailoring several increasingly precise algorithms and synergizing them into a concerted workflow. As a result, many indirect calls can be precisely resolved in an efficient and principled fashion, thereby reducing the final, expensive refinements. This is, in spirit, similar to the well-known cocktail medical therapy. The results are encouraging — our implemented prototype called Coral can achieve similar precision versus the previous field-, flow-, and context-sensitive Andersen-style call graph construction, yet scale up to millions of lines of code for the first time, to the best of our knowledge. Moreover, by evaluating the produced call graphs through the lens of downstream clients (i.e., use-after-free detection, thin slicing, and directed grey-box fuzzing), the results show that Coral can dramatically improve their effectiveness for better vulnerability hunting, understanding, and reproduction. More excitingly, we found twelve confirmed bugs (six impacted by indirect calls) in popular systems (e.g., MariaDB), spreading across multiple historical versions.
{"title":"A Cocktail Approach to Practical Call Graph Construction","authors":"Yuandao Cai, Charles Zhang","doi":"10.1145/3622833","DOIUrl":"https://doi.org/10.1145/3622833","url":null,"abstract":"After decades of research, constructing call graphs for modern C-based software remains either imprecise or inefficient when scaling up to the ever-growing complexity. The main culprit is the difficulty of resolving function pointers, as precise pointer analyses are cubic in nature and become exponential when considering calling contexts. This paper takes a practical stance by first conducting a comprehensive empirical study of function pointer manipulations in the wild. By investigating 5355 indirect calls in five popular open-source systems, we conclude that, instead of the past uniform treatments for function pointers, a cocktail approach can be more effective in “squeezing” the number of difficult pointers to a minimum using a potpourri of cheap methods. In particular, we decompose the costs of constructing highly precise call graphs of big code by tailoring several increasingly precise algorithms and synergizing them into a concerted workflow. As a result, many indirect calls can be precisely resolved in an efficient and principled fashion, thereby reducing the final, expensive refinements. This is, in spirit, similar to the well-known cocktail medical therapy. The results are encouraging — our implemented prototype called Coral can achieve similar precision versus the previous field-, flow-, and context-sensitive Andersen-style call graph construction, yet scale up to millions of lines of code for the first time, to the best of our knowledge. Moreover, by evaluating the produced call graphs through the lens of downstream clients (i.e., use-after-free detection, thin slicing, and directed grey-box fuzzing), the results show that Coral can dramatically improve their effectiveness for better vulnerability hunting, understanding, and reproduction. More excitingly, we found twelve confirmed bugs (six impacted by indirect calls) in popular systems (e.g., MariaDB), spreading across multiple historical versions.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose an automated approach to finding correct and efficient memoization algorithms from a given declarative specification. This problem has two major challenges: (i) a memoization algorithm is too large to be handled by conventional program synthesizers; (ii) we need to guarantee the efficiency of the memoization algorithm. To address this challenge, we structure the synthesis of memoization algorithms by introducing the local objective function and the memoization partition function and reduce the synthesis task to two smaller independent program synthesis tasks. Moreover, the number of distinct outputs of the function synthesized in the second synthesis task also decides the efficiency of the synthesized memoization algorithm, and we only need to minimize the number of different output values of the synthesized function. However, the generated synthesis task is still too complex for existing synthesizers. Thus, we propose a novel synthesis algorithm that combines the deductive and inductive methods to solve these tasks. To evaluate our algorithm, we collect 42 real-world benchmarks from Leetcode, the National Olympiad in Informatics in Provinces-Junior (a national-wide algorithmic programming contest in China), and previous approaches. Our approach successfully synhesizes 39/42 problems in a reasonable time, outperforming the baselines.
{"title":"Synthesizing Efficient Memoization Algorithms","authors":"Yican Sun, Xuanyu Peng, Yingfei Xiong","doi":"10.1145/3622800","DOIUrl":"https://doi.org/10.1145/3622800","url":null,"abstract":"In this paper, we propose an automated approach to finding correct and efficient memoization algorithms from a given declarative specification. This problem has two major challenges: (i) a memoization algorithm is too large to be handled by conventional program synthesizers; (ii) we need to guarantee the efficiency of the memoization algorithm. To address this challenge, we structure the synthesis of memoization algorithms by introducing the local objective function and the memoization partition function and reduce the synthesis task to two smaller independent program synthesis tasks. Moreover, the number of distinct outputs of the function synthesized in the second synthesis task also decides the efficiency of the synthesized memoization algorithm, and we only need to minimize the number of different output values of the synthesized function. However, the generated synthesis task is still too complex for existing synthesizers. Thus, we propose a novel synthesis algorithm that combines the deductive and inductive methods to solve these tasks. To evaluate our algorithm, we collect 42 real-world benchmarks from Leetcode, the National Olympiad in Informatics in Provinces-Junior (a national-wide algorithmic programming contest in China), and previous approaches. Our approach successfully synhesizes 39/42 problems in a reasonable time, outperforming the baselines.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136115200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pengfei Gao, Yedi Zhang, Fu Song, Taolue Chen, Francois-Xavier Standaert
Masking is one of the most effective countermeasures for securely implementing cryptographic algorithms against power side-channel attacks, the design of which however turns out to be intricate and error-prone. While techniques have been proposed to rigorously verify implementations of cryptographic algorithms, currently they are limited in scalability. To address this issue, compositional approaches have been investigated, but insofar they fail to prove the security of recent efficient implementations. To fill this gap, we propose a novel compositional verification approach. In particular, we introduce two new language-level security notions based on which we propose composition strategies and verification algorithms. Our approach is able to prove efficient implementations, which cannot be done by prior compositional approaches. We implement our approach as a tool CONVINCE and conduct extensive experiments to confirm its efficacy. We also use CONVINCE to further explore the design space of the AES Sbox with least refreshing by replacing its implementation for finite-field multiplication with more efficient counterparts. We automatically prove leakage-freeness of these new versions. As a result, we can effectively reduce 1,600 randomness and 3,200 XOR-operations of the state-of-the-art AES implementation.
{"title":"Compositional Verification of Efficient Masking Countermeasures against Side-Channel Attacks","authors":"Pengfei Gao, Yedi Zhang, Fu Song, Taolue Chen, Francois-Xavier Standaert","doi":"10.1145/3622862","DOIUrl":"https://doi.org/10.1145/3622862","url":null,"abstract":"Masking is one of the most effective countermeasures for securely implementing cryptographic algorithms against power side-channel attacks, the design of which however turns out to be intricate and error-prone. While techniques have been proposed to rigorously verify implementations of cryptographic algorithms, currently they are limited in scalability. To address this issue, compositional approaches have been investigated, but insofar they fail to prove the security of recent efficient implementations. To fill this gap, we propose a novel compositional verification approach. In particular, we introduce two new language-level security notions based on which we propose composition strategies and verification algorithms. Our approach is able to prove efficient implementations, which cannot be done by prior compositional approaches. We implement our approach as a tool CONVINCE and conduct extensive experiments to confirm its efficacy. We also use CONVINCE to further explore the design space of the AES Sbox with least refreshing by replacing its implementation for finite-field multiplication with more efficient counterparts. We automatically prove leakage-freeness of these new versions. As a result, we can effectively reduce 1,600 randomness and 3,200 XOR-operations of the state-of-the-art AES implementation.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136115985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anjali Pal, Brett Saiki, Ryan Tjoa, Cynthia Richey, Amy Zhu, Oliver Flatt, Max Willsey, Zachary Tatlock, Chandrakana Nandi
Rewrite rules are critical in equality saturation, an increasingly popular technique in optimizing compilers, synthesizers, and verifiers. Unfortunately, developing high-quality rulesets is difficult and error-prone. Recent work on automatically inferring rewrite rules does not scale to large terms or grammars, and existing rule inference tools are monolithic and opaque. Equality saturation users therefore struggle to guide inference and incrementally construct rulesets. As a result, most users still manually develop and maintain rulesets. This paper proposes Enumo, a new domain-specific language for programmable theory exploration. Enumo provides a small set of core operators that enable users to strategically guide rule inference and incrementally build rulesets. Short Enumo programs easily replicate results from state-of-the-art tools, but Enumo programs can also scale to infer deeper rules from larger grammars than prior approaches. Its composable operators even facilitate developing new strategies for ruleset inference. We introduce a new fast-forwarding strategy that does not require evaluating terms in the target language, and can thus support domains that were out of scope for prior work. We evaluate Enumo and fast-forwarding across a variety of domains. Compared to state-of-the-art techniques, enumo can synthesize better rulesets over a diverse set of domains, in some cases matching the effects of manually-developed rulesets in systems driven by equality saturation.
{"title":"Equality Saturation Theory Exploration à la Carte","authors":"Anjali Pal, Brett Saiki, Ryan Tjoa, Cynthia Richey, Amy Zhu, Oliver Flatt, Max Willsey, Zachary Tatlock, Chandrakana Nandi","doi":"10.1145/3622834","DOIUrl":"https://doi.org/10.1145/3622834","url":null,"abstract":"Rewrite rules are critical in equality saturation, an increasingly popular technique in optimizing compilers, synthesizers, and verifiers. Unfortunately, developing high-quality rulesets is difficult and error-prone. Recent work on automatically inferring rewrite rules does not scale to large terms or grammars, and existing rule inference tools are monolithic and opaque. Equality saturation users therefore struggle to guide inference and incrementally construct rulesets. As a result, most users still manually develop and maintain rulesets. This paper proposes Enumo, a new domain-specific language for programmable theory exploration. Enumo provides a small set of core operators that enable users to strategically guide rule inference and incrementally build rulesets. Short Enumo programs easily replicate results from state-of-the-art tools, but Enumo programs can also scale to infer deeper rules from larger grammars than prior approaches. Its composable operators even facilitate developing new strategies for ruleset inference. We introduce a new fast-forwarding strategy that does not require evaluating terms in the target language, and can thus support domains that were out of scope for prior work. We evaluate Enumo and fast-forwarding across a variety of domains. Compared to state-of-the-art techniques, enumo can synthesize better rulesets over a diverse set of domains, in some cases matching the effects of manually-developed rulesets in systems driven by equality saturation.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136115987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thierry Renaux, Sam Van den Vonder, Wolfgang De Meuter
Replicated Data Types (RDTs) are a type of data structure that can be replicated over a network, where each replica can be kept (eventually) consistent with the other replicas. They are used in applications with intermittent network connectivity, since local (offline) edits can later be merged with the other replicas. Applications that want to use RDTs often have an inherent security component that restricts data access for certain clients. However, access control for RDTs is difficult to enforce for clients that are not running within a secure environment, e.g., web applications where the client-side software can be freely tampered with. In essence, an application cannot prevent a client from reading data which they are not supposed to read, and any malicious changes will also affect well-behaved clients. This paper proposes Secure RDTs (SRDTs), a data type that specifies role-based access control for offline-available JSON data. In brief, a trusted application server specifies a security policy based on roles with read and write privileges for certain fields of an SRDT. The server enforces read privileges by projecting the data and security policy to omit any non-readable fields for the user's given role, and it acts as an intermediary to enforce write privileges. The approach is presented as an operational semantics engineered in PLT Redex, which is validated by formal proofs and randomised testing in Redex to ensure that the formal specification is secure.
{"title":"Secure RDTs: Enforcing Access Control Policies for Offline Available JSON Data","authors":"Thierry Renaux, Sam Van den Vonder, Wolfgang De Meuter","doi":"10.1145/3622802","DOIUrl":"https://doi.org/10.1145/3622802","url":null,"abstract":"Replicated Data Types (RDTs) are a type of data structure that can be replicated over a network, where each replica can be kept (eventually) consistent with the other replicas. They are used in applications with intermittent network connectivity, since local (offline) edits can later be merged with the other replicas. Applications that want to use RDTs often have an inherent security component that restricts data access for certain clients. However, access control for RDTs is difficult to enforce for clients that are not running within a secure environment, e.g., web applications where the client-side software can be freely tampered with. In essence, an application cannot prevent a client from reading data which they are not supposed to read, and any malicious changes will also affect well-behaved clients. This paper proposes Secure RDTs (SRDTs), a data type that specifies role-based access control for offline-available JSON data. In brief, a trusted application server specifies a security policy based on roles with read and write privileges for certain fields of an SRDT. The server enforces read privileges by projecting the data and security policy to omit any non-readable fields for the user's given role, and it acts as an intermediary to enforce write privileges. The approach is presented as an operational semantics engineered in PLT Redex, which is validated by formal proofs and randomised testing in Redex to ensure that the formal specification is secure.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136116376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jacob Laurel, Siyuan Brant Qian, Gagandeep Singh, Sasa Misailovic
We present Pasado, a technique for synthesizing precise static analyzers for Automatic Differentiation. Our technique allows one to automatically construct a static analyzer specialized for the Chain Rule, Product Rule, and Quotient Rule computations for Automatic Differentiation in a way that abstracts all of the nonlinear operations of each respective rule simultaneously. By directly synthesizing an abstract transformer for the composite expressions of these 3 most common rules of AD, we are able to obtain significant precision improvement compared to prior works which compose standard abstract transformers together suboptimally. We prove our synthesized static analyzers sound and additionally demonstrate the generality of our approach by instantiating these AD static analyzers with different nonlinear functions, different abstract domains (both intervals and zonotopes) and both forward-mode and reverse-mode AD. We evaluate Pasado on multiple case studies, namely soundly computing bounds on a neural network’s local Lipschitz constant, soundly bounding the sensitivities of financial models, certifying monotonicity, and lastly, bounding sensitivities of the solutions of differential equations from climate science and chemistry for verified ranges of initial conditions and parameters. The local Lipschitz constants computed by Pasado on our largest CNN are up to 2750× more precise compared to the existing state-of-the-art zonotope analysis. The bounds obtained on the sensitivities of the climate, chemical, and financial differential equation solutions are between 1.31 − 2.81× more precise (on average) compared to a state-of-the-art zonotope analysis.
{"title":"Synthesizing Precise Static Analyzers for Automatic Differentiation","authors":"Jacob Laurel, Siyuan Brant Qian, Gagandeep Singh, Sasa Misailovic","doi":"10.1145/3622867","DOIUrl":"https://doi.org/10.1145/3622867","url":null,"abstract":"We present Pasado, a technique for synthesizing precise static analyzers for Automatic Differentiation. Our technique allows one to automatically construct a static analyzer specialized for the Chain Rule, Product Rule, and Quotient Rule computations for Automatic Differentiation in a way that abstracts all of the nonlinear operations of each respective rule simultaneously. By directly synthesizing an abstract transformer for the composite expressions of these 3 most common rules of AD, we are able to obtain significant precision improvement compared to prior works which compose standard abstract transformers together suboptimally. We prove our synthesized static analyzers sound and additionally demonstrate the generality of our approach by instantiating these AD static analyzers with different nonlinear functions, different abstract domains (both intervals and zonotopes) and both forward-mode and reverse-mode AD. We evaluate Pasado on multiple case studies, namely soundly computing bounds on a neural network’s local Lipschitz constant, soundly bounding the sensitivities of financial models, certifying monotonicity, and lastly, bounding sensitivities of the solutions of differential equations from climate science and chemistry for verified ranges of initial conditions and parameters. The local Lipschitz constants computed by Pasado on our largest CNN are up to 2750× more precise compared to the existing state-of-the-art zonotope analysis. The bounds obtained on the sensitivities of the climate, chemical, and financial differential equation solutions are between 1.31 − 2.81× more precise (on average) compared to a state-of-the-art zonotope analysis.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136078032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chengcheng Wan, Yuhan Liu, Kuntai Du, Henry Hoffmann, Junchen Jiang, Michael Maire, Shan Lu
Due to the under-specified interfaces, developers face challenges in correctly integrating machine learning (ML) APIs in software. Even when the ML API and the software are well designed on their own, the resulting application misbehaves when the API output is incompatible with the software. It is desirable to have an adapter that converts ML API output at runtime to better fit the software need and prevent integration failures. In this paper, we conduct an empirical study to understand ML API integration problems in real-world applications. Guided by this study, we present SmartGear, a tool that automatically detects and converts mismatching or incorrect ML API output at run time, serving as a middle layer between ML API and software. Our evaluation on a variety of open-source applications shows that SmartGear detects 70% incompatible API outputs and prevents 67% potential integration failures, outperforming alternative solutions.
{"title":"Run-Time Prevention of Software Integration Failures of Machine Learning APIs","authors":"Chengcheng Wan, Yuhan Liu, Kuntai Du, Henry Hoffmann, Junchen Jiang, Michael Maire, Shan Lu","doi":"10.1145/3622806","DOIUrl":"https://doi.org/10.1145/3622806","url":null,"abstract":"Due to the under-specified interfaces, developers face challenges in correctly integrating machine learning (ML) APIs in software. Even when the ML API and the software are well designed on their own, the resulting application misbehaves when the API output is incompatible with the software. It is desirable to have an adapter that converts ML API output at runtime to better fit the software need and prevent integration failures. In this paper, we conduct an empirical study to understand ML API integration problems in real-world applications. Guided by this study, we present SmartGear, a tool that automatically detects and converts mismatching or incorrect ML API output at run time, serving as a middle layer between ML API and software. Our evaluation on a variety of open-source applications shows that SmartGear detects 70% incompatible API outputs and prevents 67% potential integration failures, outperforming alternative solutions.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Synthesizing relational queries from data is challenging in the presence of recursion and invented predicates. We propose a fully automated approach to synthesize such queries. Our approach comprises of two steps: it first synthesizes a non-recursive query consistent with the given data, and then identifies recursion schemes in it and thereby generalizes to arbitrary data. This generalization is achieved by an iterative predicate unification procedure which exploits the notion of data provenance to accelerate convergence. In each iteration of the procedure, a constraint solver proposes a candidate query, and a query evaluator checks if the proposed program is consistent with the given data. The data provenance for a failed query allows us to construct additional constraints for the constraint solver and refine the search. We have implemented our approach in a tool named Mobius. On a suite of 21 challenging recursive query synthesis tasks, Mobius outperforms three state-of-the-art baselines Gensynth, ILASP, and Popper, both in terms of runtime and accuracy. We also demonstrate that the synthesized queries generalize well to unseen data.
{"title":"Mobius: Synthesizing Relational Queries with Recursive and Invented Predicates","authors":"Aalok Thakkar, Nathaniel Sands, George Petrou, Rajeev Alur, Mayur Naik, Mukund Raghothaman","doi":"10.1145/3622847","DOIUrl":"https://doi.org/10.1145/3622847","url":null,"abstract":"Synthesizing relational queries from data is challenging in the presence of recursion and invented predicates. We propose a fully automated approach to synthesize such queries. Our approach comprises of two steps: it first synthesizes a non-recursive query consistent with the given data, and then identifies recursion schemes in it and thereby generalizes to arbitrary data. This generalization is achieved by an iterative predicate unification procedure which exploits the notion of data provenance to accelerate convergence. In each iteration of the procedure, a constraint solver proposes a candidate query, and a query evaluator checks if the proposed program is consistent with the given data. The data provenance for a failed query allows us to construct additional constraints for the constraint solver and refine the search. We have implemented our approach in a tool named Mobius. On a suite of 21 challenging recursive query synthesis tasks, Mobius outperforms three state-of-the-art baselines Gensynth, ILASP, and Popper, both in terms of runtime and accuracy. We also demonstrate that the synthesized queries generalize well to unseen data.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a novel method, called WheaCha, for explaining the predictions of code models. Similar to attribution methods, WheaCha seeks to identify input features that are responsible for a particular prediction that models make. On the other hand, it differs from attribution methods in crucial ways. Specifically, WheaCha separates an input program into "wheat" (i.e., defining features that are the reason for which models predict the label that they predict) and the rest "chaff" for any given prediction. We realize WheaCha in a tool, HuoYan, and use it to explain four prominent code models: code2vec, seq-GNN, GGNN, and CodeBERT. Results show that (1) HuoYan is efficient — taking on average under twenty seconds to compute wheat for an input program in an end-to-end fashion (i.e., including model prediction time); (2) the wheat that all models use to make predictions is predominantly comprised of simple syntactic or even lexical properties (i.e., identifier names); (3) neither the latest explainability methods for code models (i.e., SIVAND and CounterFactual Explanations) nor the most noteworthy attribution methods (i.e., Integrated Gradients and SHAP) can precisely capture wheat. Finally, we set out to demonstrate the usefulness of WheaCha, in particular, we assess if WheaCha’s explanations can help end users to identify defective code models (e.g., trained on mislabeled data or learned spurious correlations from biased data). We find that, with WheaCha, users achieve far higher accuracy in identifying faulty models than SIVAND, CounterFactual Explanations, Integrated Gradients and SHAP.
{"title":"An Explanation Method for Models of Code","authors":"Yu Wang, Ke Wang, Linzhang Wang","doi":"10.1145/3622826","DOIUrl":"https://doi.org/10.1145/3622826","url":null,"abstract":"This paper introduces a novel method, called WheaCha, for explaining the predictions of code models. Similar to attribution methods, WheaCha seeks to identify input features that are responsible for a particular prediction that models make. On the other hand, it differs from attribution methods in crucial ways. Specifically, WheaCha separates an input program into \"wheat\" (i.e., defining features that are the reason for which models predict the label that they predict) and the rest \"chaff\" for any given prediction. We realize WheaCha in a tool, HuoYan, and use it to explain four prominent code models: code2vec, seq-GNN, GGNN, and CodeBERT. Results show that (1) HuoYan is efficient — taking on average under twenty seconds to compute wheat for an input program in an end-to-end fashion (i.e., including model prediction time); (2) the wheat that all models use to make predictions is predominantly comprised of simple syntactic or even lexical properties (i.e., identifier names); (3) neither the latest explainability methods for code models (i.e., SIVAND and CounterFactual Explanations) nor the most noteworthy attribution methods (i.e., Integrated Gradients and SHAP) can precisely capture wheat. Finally, we set out to demonstrate the usefulness of WheaCha, in particular, we assess if WheaCha’s explanations can help end users to identify defective code models (e.g., trained on mislabeled data or learned spurious correlations from biased data). We find that, with WheaCha, users achieve far higher accuracy in identifying faulty models than SIVAND, CounterFactual Explanations, Integrated Gradients and SHAP.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：