C-CORE:Clustering by Code Representation to Prioritize Test Cases in Compiler Testing

Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-CORE increases the average of the percentage of faults detected(APFD)value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases.When compared to the best results from approaches using predicted code coverage,C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%.

Algorithms for Pre-Compiling Programs by Parallel Compilers

The paper addresses the challenge of transmitting a big number offiles stored in a data center(DC),encrypting them by compilers,and sending them through a network at an acceptable time.Face to the big number offiles,only one compiler may not be sufficient to encrypt data in an acceptable time.In this paper,we consider the problem of several compilers and the objective is tofind an algorithm that can give an efficient schedule for the givenfiles to be compiled by the compilers.The main objective of the work is to minimize the gap in the total size of assignedfiles between compilers.This minimization ensures the fair distribution offiles to different compilers.This problem is considered to be a very hard problem.This paper presents two research axes.Thefirst axis is related to architecture.We propose a novel pre-compiler architecture in this context.The second axis is algorithmic development.We develop six algorithms to solve the problem,in this context.These algorithms are based on the dispatching rules method,decomposition method,and an iterative approach.These algorithms give approximate solutions for the studied problem.An experimental result is imple-mented to show the performance of algorithms.Several indicators are used to measure the performance of the proposed algorithms.In addition,five classes are proposed to test the algorithms with a total of 2350 instances.A comparison between the proposed algorithms is presented in different tables discussed to show the performance of each algorithm.The result showed that the best algorithm is the Iterative-mixed Smallest-Longest-Heuristic(ISL)with a percentage equal to 97.7%and an average running time equal to 0.148 s.All other algorithms did not exceed 22%as a percentage.The best algorithm excluding ISL is Iterative-mixed Longest-Smallest Heuristic(ILS)with a percentage equal to 21,4%and an average running time equal to 0.150 s.

Constructing an AI Compiler for ARM Cortex-M Devices

The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code,which often requires specific treatment for each platform.The problem becomes critical on embedded devices,where computational and memory resources are strictly constrained.Compilers play an essential role in deploying source code on a target device through the backend.In this work,a novel backend for the Open Neural Network Compiler(ONNC)is proposed,which exploits machine learning to optimize code for the ARM Cortex-M device.The backend requires minimal changes to Open Neural Network Exchange(ONNX)models.Several novel optimization techniques are also incorporated in the backend,such as quantizing the ONNX model’s weight and automatically tuning the dimensions of operators in computations.The performance of the proposed framework is evaluated for two applications:handwritten digit recognition on the Modified National Institute of Standards and Technology(MNIST)dataset and model,and image classification on the Canadian Institute For Advanced Research and 10(CIFAR-10)dataset with the AlexNet-Light model.The system achieves 98.90%and 90.55%accuracy for handwritten digit recognition and image classification,respectively.Furthermore,the proposed architecture is significantly more lightweight than other state-of-theart models in terms of both computation time and generated source code complexity.From the system perspective,this work provides a novel approach to deploying direct computations from the available ONNX models to target devices by optimizing compilers while maintaining high efficiency in accuracy performance.

IDL Compiler API Design, Application and Validation Based on XML API

In order to adapt different languages and platforms, the paper discusses how to process and validate IDL symbol table and intermediate code by XML API. It puts emphasis on IDL AP1 extension towards DOM API based on the idea of combining XML with IDL compilers. At last, the IDL compiler designing framework based on XML AP! is given, in which compiler front end can be managed and validated by some XML techniques and tools, IDL API can be validated on the basis of test, so IDL intermediate code is provided with maintainability, portability and generation. IDL compiler can be developed and extended by XML-based API, which realizes versatility and portability of modern compiler.

Compiler IR-Based Program Encoding Method for Software Defect Prediction

With the continuous expansion of software applications,people’s requirements for software quality are increasing.Software defect prediction is an important technology to improve software quality.It often encodes the software into several features and applies the machine learning method to build defect prediction classifiers,which can estimate the software areas is clean or buggy.However,the current encoding methods are mainly based on the traditional manual features or the AST of source code.Traditional manual features are difficult to reflect the deep semantics of programs,and there is a lot of noise information in AST,which affects the expression of semantic features.To overcome the above deficiencies,we combined with the Convolutional Neural Networks(CNN)and proposed a novel compiler Intermediate Representation(IR)based program encoding method for software defect prediction(CIR-CNN).Specifically,our program encoding method is based on the compiler IR,which can eliminate a large amount of noise information in the syntax structure of the source code and facilitate the acquisition of more accurate semantic information.Secondly,with the help of data flow analysis,a Data Dependency Graph(DDG)is constructed on the compiler IR,which helps to capture the deeper semantic information of the program.Finally,we use the widely used CNN model to build a software defect prediction model,which can increase the adaptive ability of the method.To evaluate the performance of the CIR-CNN,we use seven projects from PROMISE datasets to set up comparative experiments.The experiments results show that,in WPDP,with our CIR-CNN method,the prediction accuracy was improved by 12%for the AST-encoded CNN-based model and by 20.9%for the traditional features-based LR model,respectively.And in CPDP,the AST-encoded DBNbased model was improved by 9.1%and the traditional features-based TCA+model by 19.2%,respectively.

Constructing a Simple Verbal Compiler

The paper’s purpose is to design and program the four operation-calculators that receives voice instructions and runs them as either a voice or text phase. The Calculator simulates the work of the Compiler. The paper is a practical example programmed to support that it is possible to construct a verbal Compiler.

AN OBJECT ORIENTED C++ PARALLEL COMPILER SYSTEM

An object-oriented C++ parallel compiler System, called OOCPCS, is developed to facilitate programmers to write sequential programs using C++ or Annotated C++ language for parallel computahon. OOCPCS bases on an integrated object-oriented paradigm and large-grain data flow model, called OOLGDFM, and recognizes automatically parallel objects using parallel compiling techniques. The paper describes the object-oriented parallel model and realization of the System on networks.

A High Speed Signal Processing Machine -Its Architecture, Language and Compiler

A systolic array architecture computer (FXCQ) has been designed for signal processing. R can handle floating point data at very high speed. It is composed of 16 processing cells and a cache that are connected linearly and form a ring structure. All processing cells are identical and programmable. Each processing cell has the peak performance of 20 million floating-point operations per second (20MFLOPS). The machine therefore has a peak performance of 320 M FLOPS. It is integrated as an attached processor into a host system through VME bus interface. Programs for FXCQ are written in a high-level language -B language, which is supported by a parallel optimizing compiler. This paper describes the architecture of FXCQ, B language and its compiler.

高性能计算与标准化的完美结合——Intel Compiler 8.0及其配套工具介绍

在x86体系结构的PC里,编译代码质量最好的C++是谁,不是VC++,不是Borland C++,也不是g++,而是InterC++。本文将带你畅游这个高性能编译器的世界。体会一下无需修改程序就可提升20％性能的美妙感觉。

The Implementation of the ASN.1-C++ Compiler and Its Application in IN

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Shared Variable Oriented Parallel Precompiler for SPMD Model

For the moment, commercial parallel computer systems with distributed memory architecture are usually provided with parallel FORTRAN or parallel C compliers, which are just traditional sequential FORTRAN or C compilers expanded with communication statements. Programmers suffer from writing parallel programs with communication statements.The Shared Variable Oriented Parallel Precompiler (SVOPP) proposed in this paper can automatically generate appropriate communication statements based on shared variables for SPMD (Single Program Multiple Data) computa-tion model and greatly ease the parallel programming with high communication efficiency. The core function of parallel C precompi1er has been successfully veri-fied on a transputer-based parallel computer. Its prominent performance shows that SVOPP is probably a break-through in parallel programming technique.

Formal verification of synchronous data-flow program transformations toward certified compilers

Translation validation was invented in the 90＇s by Pnueli et al. as a technique to formally verify the correctness of code generators. Rather than certifying the code generator or exhaustively qualifying it, translation validators attempt to verify that program transformations preserve semantics. In this work, we adopt this approach to formally verify that the clock semantics and data dependence are preserved during the compilation of the Signal compiler. Translation valida- tion is implemented for every compilation phase from the initial phase until the latest phase where the executable code is generated, by proving the transformation in each phase of the compiler preserves the semantics. We represent the clock semantics, the data dependence of a program and its trans- formed counterpart as first-order formulas which are called clock models and synchronous dependence graphs （SDGs）, respectively. We then introduce clock refinement and depen- dence refinement relations which express the preservations of clock semantics and dependence, as a relation on clock mod- els and SDGs, respectively. Our validator does not require any instrumentation or modification of the compiler, nor any rewriting of the source program.

Towards a verified compiler prototype for the synchronous language SIGNAL

SIGNAL belongs to the synchronous languages family which are widely used in the design of safety-critical real-time systems such as avionics, space systems, and nu- clear power plants. This paper reports a compiler prototype for SIGNAL. Compared with the existing SIGNAL com- piler, we propose a new intermediate representation （named S-CGA, a variant of clocked guarded actions）, to integrate more synchronous programs into our compiler prototype in the future. The front-end of the compiler, i.e., the transla- tion from SIGNAL to S-CGA, is presented. As well, the proof of semantics preservation is mechanized in the theo- rem prover Coq. Moreover, we present the back-end of the compiler, including sequential code generation and multi- threaded code generation with time-predictable properties. With the rising importance of multi-core processors in safety- critical embedded systems or cyber-physical systems （CPS）, there is a growing need for model-driven generation of multi- threaded code and thus mapping on multi-core. We propose a time-predictable multi-core architecture model in archi- tecture analysis and design language （AADL）, and map the multi-threaded code to this model.

NUAPC: A Parallelizing Compiler for C++

This paper presents a model for automatically parallelizing compiler based on C++ which consists of compile-time and run-time parallelizing facilities.The paper also describes a method for finding both intra-object and inter-object parallelism. The parallelism detection is completely transparent to users.

Design and Implementation of Java Just-in-Time Compiler

Early Java implementations relied on interpretation, leading to poor performance compared to compiled programs. Java just-in-time (JIT) compiler can compile Java programs at runtime, so it not only improves Java's performance prominently, but also preserves Java's portability. In this paper the design and implementing techniques of Java JIT compiler based on Chinese open system are discussed in detail. To enhance the portability, a translating method which combines the static simulating method and macro expansion method is adopted. The optimization technique for JIT compiler is also discussed and a way to evaluate the hotspots in Java programs is presented. Experiments have been conducted to verify JIT compilation technique as an efficient way to accelerate Java.

Modified condition/decision coverage(MC/DC)oriented compiler optimization for symbolic execution

Symbolic execution is an effective way of systematically exploring the search space of a program,and is often used for automatic software testing and bug finding.The program to be analyzed is usually compiled into a binary or an intermediate representation,on which symbolic execution is carried out.During this process,compiler optimizations influence the effectiveness and efficiency of symbolic execution.However,to the best of our knowledge,there exists no work on compiler optimization recommendation for symbolic execution with respect to(w.r.t.)modified condition/decision coverage(MC/DC),which is an important testing coverage criterion widely used for mission-critical software.This study describes our use of a state-of-the-art symbolic execution tool to carry out extensive experiments to study the impact of compiler optimizations on symbolic execution w.r.t.MC/DC.The results indicate that instruction combining(IC)optimization is the important and dominant optimization for symbolic execution w.r.t.MC/DC.We designed and implemented a support vector machine based optimization recommendation method w.r.t.IC(denoted as auto).The experiments on two standard benchmarks(Coreutils and NECLA)showed that auto achieves the best MC/DC on 67.47%of Coreutils programs and 78.26%of NECLA programs.