DOCSLIB.ORG

Framework for Combined Compile-Time and Runtime Error Checking in Open64

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Framework for Combined Compile-Time and Runtime Error Checking in Open64 Framework for combined compile-time and runtime error checking in Open64 Jianxin Lai, Peng Yuan, Hongmei Wang, Dingfei Zhang Unix System Lab, Hewlett-Packard Company {Jianxin.lai, peng.yuan, hong-mei.wang, zhang.ding-fei}@hp.com Abstract inter-procedural analysis (IPA) framework. The framework provides the facility to instrument the intermediate representation A lot of techniques have been developed to detect errors in (WHIRL) for runtime checking and perform both intra- and program automatically. Static checkers scans the program source inter-procedural data flow analysis for static checking. The code to find potential problems. Runtime checkers changes the protocol to pass the analysis result from static checker to runtime executable statically by compile-time instrumentation or checker to guide the instrumentation is also developed. With this dynamically by runtime instrumentation. Static checker tends to new framework, the checkers are able to eliminate the false report false positive warnings and runtime checker shows great positive of static checker while decrease the overhead of the overhead at runtime. runtime checker. In this paper, an Open64 based framework for combined compile-time and runtime error checking is present. Based on the 2. Framework overview framework, the checker for uninitialized variables is implemented. The checker has less runtime overhead than traditional runtime Our framework includes two major components. One is the checkers and don’t issue any false positive warnings. With this runtime checker, which is based on compiler instrumentation. The framework, new checkers are able to make better trade-off program will be instrumented with the interface provided by the between false positive warnings and runtime overhead than any runtime checking library. The runtime checker is designed to traditional approaches. detect the errors in the program during the execution of the program. It’s available at all optimization levels. The other is the Categories and Subject Descriptors D.3.3 [Programming static checker. This part is built in WOPT and IPA. One major Languages]: Processors – compilers. task of the static checker is to calculate the candidate set for runtime checking and pass the information to instrumentation General Terms Algorithms phase. Static checker is able to identify the real safe cases and Keywords open64 compiler, static checker, runtime checker exclude them from the candidate set. Since the safe cases are not instrumented, the overhead of our approach is less than traditional 1. Introduction binary instrumentation based checkers. Another task is to identify the real unsafe case and issue warnings to programmer. Thus the A lot of techniques have been developed to detect errors in false positive warnings are eliminated. With the collaboration of program automatically. Basically, the corresponding tools utilize the two parts, we can make better balance between the accuracy two basic approaches for error checking. One is checking the error and overhead than before. by scanning the source code, aka static checker. The static checker The whole framework is based on existing open64 doesn’t require running the program during checking errors in the infrastructure. It includes 4 parts: program. The other is the runtime checker, which starts the - Instrumentation phase program in a managed environment and performs the checking - Library for runtime checking (librtc) while the program is running. - Static checking phase in WOPT enabled at O2 and O3 The static checker has a lot of advantages over the runtime - Static checking phase in IPA checker. It can be integrated with the development environment easily, check the program during the coding phase and is able to check part or whole of the source code regardless the program is executable or not. Static checker can also cover all code in the program regardless it is executable or not. The major disadvantage of static checker is the accuracy. Static checker may either miss real errors or issue false positive warnings. On the contrary, the runtime checker doesn’t report any false positive warnings but it usually has noticeable overhead. Also, runtime checker is only able to detect errors in the executed code. In this paper, a framework combining compile-time and runtime error checking to detect errors in program will be presented. The framework is based on Open64 [1] in order to leverage the compiler infrastructure, static single assignment form (SSA) based data flow analysis in global optimizer (WOPT) and C/C++/F 2.4 Static checking phase in IPA Open64 IPA framework enables inter-procedural analysis and optimizations. But the current IPA framework is not context FE sensitive and the support for inter-procedural data flow analysis is -IPA -O2 limited. In the new framework, the current open64 IPA infrastructure is enhanced to support context-sensitive and partial flow-sensitive inter-procedural analysis. The new IPA IPL infrastructure is flexible to control the analysis scope to be either WOPT IPA whole program or partial program. The static checkers built on the -O0/1 new IPA infrastructure are able to perform checking in a larger scope with flexible resource control. Similar to existing Open64 IPA, the new IPA infrastructure Instrument librtc also includes two phases: local summary phase (IPL) and IPA phase. In IPL phase, the summary information for the PU is collected and written into the fake object file. To achieve context CG sensitivity, each call site will be summarized separately regardless if they call the same function or not. To achieve flow sensitivity, Figure 1. Framework overview the SSA versions of function parameters and chi nodes of global variables associated to the call site are also summarized. The SSA 2.1 Instrumentation phase versions of global variables on the RETURN statement are The instrumentation phase is activated at all optimization summarized to model the side effect of the function. Also, the levels. In general, it walks the whole WHIRL tree and instruments checker can have an IPL phase to summarize the information the WHIRL node based on the checker’s requirement. This phase needed for the IPA phase. In IPA phase, the call graph will be also creates new symbols and makes the right layout for all built using the call site summary information. On the call graph, variables used by the checking functions defined in the runtime the SSA versions of actual and chi nodes of global variables will library. This phase includes 2 steps. Step 1 is the candidate be applied to the edges of the call graph. Once the call graph is selection. If the static checker in either WOPT or IPA is available, built, the SSA versions of the RETURN statement will be applied candidate set is provided by the static checker. Otherwise all to the call graph using a bottom-up approach. All chi nodes on the possible cases will be included in the candidate set. Step 2 is the edge will be checked. If the symbol of the chi is not changed in transformation. All candidates will be instrumented for runtime the callee, the result version of the chi node will be replaced by checking. the operand version. All occurrences of the result version will be replaced in all the summary information of the caller. Then we 2.2 Library for runtime checking (librtc) can treat the call graph as control flow graph and perform classic The runtime library provides the functionality to check errors SSA construction algorithm on it to construct the global SSA. If at runtime. For different checkers, different checking functions are the callee has multiple call sites, a entry phi node will be needed. Also, the instrumentation phase requires the interfaces introduced to replace the entry chi. The number of the operands of provided by the librtc to generate the right function call at the the entry phi node equals to the number of in-edges of the callee. right position on the IR. The version of the operand matches to the result version of chi node on the edge. 2.3 Static checking phase in WOPT WOPT builds hashed static single assignment form (HSSA) and does data flow analysis on top of the HSSA representation [2]. Symbol resolution Entry PHI insertion On HSSA representation, the alias information is represented by chi nodes and mu nodes. The chi nodes are created for global variables around the call sites to model the side effects of the Symtab merge Rename global SSA function call. The mu nodes are created for global variables and attached to the RETURN statement. The entry chi is inserted to the beginning of the program unit (PU) to make sure all uses have Build Callgraph Update summary the definitions. In our framework, the version defined by entry chi is assigned with a special version number. If the call site uses the Propagate special version in either parameters or the chi attached to the call IPA checking node, the variable is not changed on all paths from the function RETURN version entry to the call site. If the mu node on the RETURN statement uses the special version, the variable is not changed in all paths from the entry to exit. Figure 2. IPA phase ordering The framework is built on top of WOPT. It provides methods to traverse the symbol tables, HSSA representation, In the figure 3 below, (A) is the source code. (B) is the Definition-Use (DU) manager and alias manager. The checkers in intra-procedural SSA. (C) is the call graph before applying the WOPT are able to visit the IR and symbol tables by these global SSA construction. (D) is the global SSA representation. interfaces to perform the intra-procedural checking. if the variable is defined or not. At the present of IPA, if the int dbg; int dbg; variable is passed to other functions, a new parameter will be int foo(int x) { int foo(int x) { appended to the end of the parameter list and the address of the int y; int y; flag will be passed by this new parameter.
Load more

Recommended publications
  • The LLVM Instruction Set and Compilation Strategy
    The LLVM Instruction Set and Compilation Strategy Chris Lattner Vikram Adve University of Illinois at Urbana-Champaign lattner,vadve ¡ @cs.uiuc.edu Abstract This document introduces the LLVM compiler infrastructure and instruction set, a simple approach that enables sophisticated code transformations at link time, runtime, and in the field. It is a pragmatic approach to compilation, interfering with programmers and tools as little as possible, while still retaining extensive high-level information from source-level compilers for later stages of an application’s lifetime. We describe the LLVM instruction set, the design of the LLVM system, and some of its key components. 1 Introduction Modern programming languages and software practices aim to support more reliable, flexible, and powerful software applications, increase programmer productivity, and provide higher level semantic information to the compiler. Un- fortunately, traditional approaches to compilation either fail to extract sufficient performance from the program (by not using interprocedural analysis or profile information) or interfere with the build process substantially (by requiring build scripts to be modified for either profiling or interprocedural optimization). Furthermore, they do not support optimization either at runtime or after an application has been installed at an end-user’s site, when the most relevant information about actual usage patterns would be available. The LLVM Compilation Strategy is designed to enable effective multi-stage optimization (at compile-time, link-time, runtime, and offline) and more effective profile-driven optimization, and to do so without changes to the traditional build process or programmer intervention. LLVM (Low Level Virtual Machine) is a compilation strategy that uses a low-level virtual instruction set with rich type information as a common code representation for all phases of compilation.
    [Show full text]
  • The Interplay of Compile-Time and Run-Time Options for Performance Prediction Luc Lesoil, Mathieu Acher, Xhevahire Tërnava, Arnaud Blouin, Jean-Marc Jézéquel
    The Interplay of Compile-time and Run-time Options for Performance Prediction Luc Lesoil, Mathieu Acher, Xhevahire Tërnava, Arnaud Blouin, Jean-Marc Jézéquel To cite this version: Luc Lesoil, Mathieu Acher, Xhevahire Tërnava, Arnaud Blouin, Jean-Marc Jézéquel. The Interplay of Compile-time and Run-time Options for Performance Prediction. SPLC 2021 - 25th ACM Inter- national Systems and Software Product Line Conference - Volume A, Sep 2021, Leicester, United Kingdom. pp.1-12, 10.1145/3461001.3471149. hal-03286127 HAL Id: hal-03286127 https://hal.archives-ouvertes.fr/hal-03286127 Submitted on 15 Jul 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. The Interplay of Compile-time and Run-time Options for Performance Prediction Luc Lesoil, Mathieu Acher, Xhevahire Tërnava, Arnaud Blouin, Jean-Marc Jézéquel Univ Rennes, INSA Rennes, CNRS, Inria, IRISA Rennes, France [email protected] ABSTRACT Both compile-time and run-time options can be configured to reach Many software projects are configurable through compile-time op- specific functional and performance goals. tions (e.g., using ./configure) and also through run-time options (e.g., Existing studies consider either compile-time or run-time op- command-line parameters, fed to the software at execution time).
    [Show full text]
  • Design and Implementation of Generics for the .NET Common Language Runtime
    Design and Implementation of Generics for the .NET Common Language Runtime Andrew Kennedy Don Syme Microsoft Research, Cambridge, U.K. fakeÒÒ¸d×ÝÑeg@ÑicÖÓ×ÓfغcÓÑ Abstract cally through an interface definition language, or IDL) that is nec- essary for language interoperation. The Microsoft .NET Common Language Runtime provides a This paper describes the design and implementation of support shared type system, intermediate language and dynamic execution for parametric polymorphism in the CLR. In its initial release, the environment for the implementation and inter-operation of multiple CLR has no support for polymorphism, an omission shared by the source languages. In this paper we extend it with direct support for JVM. Of course, it is always possible to “compile away” polymor- parametric polymorphism (also known as generics), describing the phism by translation, as has been demonstrated in a number of ex- design through examples written in an extended version of the C# tensions to Java [14, 4, 6, 13, 2, 16] that require no change to the programming language, and explaining aspects of implementation JVM, and in compilers for polymorphic languages that target the by reference to a prototype extension to the runtime. JVM or CLR (MLj [3], Haskell, Eiffel, Mercury). However, such Our design is very expressive, supporting parameterized types, systems inevitably suffer drawbacks of some kind, whether through polymorphic static, instance and virtual methods, “F-bounded” source language restrictions (disallowing primitive type instanti- type parameters, instantiation at pointer and value types, polymor- ations to enable a simple erasure-based translation, as in GJ and phic recursion, and exact run-time types.
    [Show full text]
  • Adding Self-Healing Capabilities to the Common Language Runtime
    Adding Self-healing capabilities to the Common Language Runtime Rean Griffith Gail Kaiser Columbia University Columbia University [email protected] [email protected] Abstract systems can leverage to maintain high system availability is to perform repairs in a degraded mode of operation[23, 10]. Self-healing systems require that repair mechanisms are Conceptually, a self-managing system is composed of available to resolve problems that arise while the system ex- four (4) key capabilities [12]; Monitoring to collect data ecutes. Managed execution environments such as the Com- about its execution and operating environment, performing mon Language Runtime (CLR) and Java Virtual Machine Analysis over the data collected from monitoring, Planning (JVM) provide a number of application services (applica- an appropriate course of action and Executing the plan. tion isolation, security sandboxing, garbage collection and Each of the four functions participating in the Monitor- structured exception handling) which are geared primar- Analyze-Plan-Execute (MAPE) loop consumes and pro- ily at making managed applications more robust. How- duces knowledgewhich is integral to the correct functioning ever, none of these services directly enables applications of the system. Over its execution lifetime the system builds to perform repairs or consistency checks of their compo- and refines a knowledge-base of its behavior and environ- nents. From a design and implementation standpoint, the ment. Information in the knowledge-base could include preferred way to enable repair in a self-healing system is patterns of resource utilization and a “scorecard” tracking to use an externalized repair/adaptation architecture rather the success of applying specific repair actions to detected or than hardwiring adaptation logic inside the system where it predicted problems.
    [Show full text]
  • 18Mca42c .Net Programming (C#)
    18MCA42C .NET PROGRAMMING (C#) Introduction to .NET Framework .NET is a software framework which is designed and developed by Microsoft. The first version of .Net framework was 1.0 which came in the year 2002. It is a virtual machine for compiling and executing programs written in different languages like C#, VB.Net etc. It is used to develop Form-based applications, Web-based applications, and Web services. There is a variety of programming languages available on the .Net platform like VB.Net and C# etc.,. It is used to build applications for Windows, phone, web etc. It provides a lot of functionalities and also supports industry standards. .NET Framework supports more than 60 programming languages in which 11 programming languages are designed and developed by Microsoft. 11 Programming Languages which are designed and developed by Microsoft are: C#.NET VB.NET C++.NET J#.NET F#.NET JSCRIPT.NET WINDOWS POWERSHELL IRON RUBY IRON PYTHON C OMEGA ASML(Abstract State Machine Language) Main Components of .NET Framework 1.Common Language Runtime(CLR): CLR is the basic and Virtual Machine component of the .NET Framework. It is the run-time environment in the .NET Framework that runs the codes and helps in making the development process easier by providing the various services such as remoting, thread management, type-safety, memory management, robustness etc.. Basically, it is responsible for managing the execution of .NET programs regardless of any .NET programming language. It also helps in the management of code, as code that targets the runtime is known as the Managed Code and code doesn’t target to runtime is known as Unmanaged code.
    [Show full text]
  • Dmon: Efficient Detection and Correction of Data Locality
    DMon: Efficient Detection and Correction of Data Locality Problems Using Selective Profiling Tanvir Ahmed Khan and Ian Neal, University of Michigan; Gilles Pokam, Intel Corporation; Barzan Mozafari and Baris Kasikci, University of Michigan https://www.usenix.org/conference/osdi21/presentation/khan This paper is included in the Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation. July 14–16, 2021 978-1-939133-22-9 Open access to the Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation is sponsored by USENIX. DMon: Efficient Detection and Correction of Data Locality Problems Using Selective Profiling Tanvir Ahmed Khan Ian Neal Gilles Pokam Barzan Mozafari University of Michigan University of Michigan Intel Corporation University of Michigan Baris Kasikci University of Michigan Abstract cally at run time. In fact, as we (§6.2) and others [2,15,20,27] Poor data locality hurts an application’s performance. While demonstrate, compiler-based techniques can sometimes even compiler-based techniques have been proposed to improve hurt performance when the assumptions made by those heuris- data locality, they depend on heuristics, which can sometimes tics do not hold in practice. hurt performance. Therefore, developers typically find data To overcome the limitations of static optimizations, the locality issues via dynamic profiling and repair them manually. systems community has invested substantial effort in devel- Alas, existing profiling techniques incur high overhead when oping dynamic profiling tools [28,38, 57,97, 102]. Dynamic used to identify data locality problems and cannot be deployed profilers are capable of gathering detailed and more accurate in production, where programs may exhibit previously-unseen execution information, which a developer can use to identify performance problems.
    [Show full text]
  • Compile Time and Runtime
    COMPILE TIME AND RUNTIME Compile time and runtime are two distinctly different times during the active life of a computer program. Compile time is when the program is compiled; runtime is when it executes (on either a physical or virtual computer). Programmers use the term static to refer to anything that is created during compile time and stays fixed during the program run. They use the term dynamic to refer to things that are created and can change during execution. Example In application MyApp shown below, the data type of variable tax is statically set to double for the entire program run. Its value is dynamically set when the assignment statement (marked **) executes. public class MyApp { public static void main( String [] args ) { double tax; . ** tax = price * TAX_RATE; . } } Compiler Tasks The main tasks of the compiler are to: Check program statements for errors and report them. Generate the machine instructions that carry out the operations specified by the program. To do this, the compiler must gather as much information as possible about the items (e.g. variables, classes, objects, etc.) used in the program. Compile Time and Runtime Page 1 Example Consider this Java assignment statement: x = y2 + z; To determine if it is correct, the compiler needs to know if y2 is a declared variable (perhaps the programmer meant to type y*2). To generate correct bytecode, the compiler needs to know the data types of the variables (integer addition is a different machine instruction than floating-point addition). Declarations Program text that is primarily meant to provide information to the compiler is called a declaration, which the compiler processes by collecting the information given.
    [Show full text]
  • Surgical Precision JIT Compilers
    Surgical Precision JIT Compilers Tiark Rompf ‡∗ Arvind K. Sujeethy Kevin J. Browny HyoukJoong Leey Hassan Chafizy Kunle Olukotuny zOracle Labs: {first.last}@oracle.com ∗EPFL: {first.last}@epfl.ch yStanford University: {asujeeth, kjbrown, hyouklee, kunle}@stanford.edu Abstract class Record(fields: Array[String], schema: Array[String]) { def apply(key: String) = fields(schema indexOf key) Just-in-time (JIT) compilation of running programs provides more def foreach(f: (String,String) => Unit) = optimization opportunities than offline compilation. Modern JIT for ((k,i) <- schema.zipWithIndex) f(k,fields(i)) compilers, such as those in virtual machines like Oracle’s HotSpot } def processCSV(file: String)(yld: Record => Unit) = { for Java or Google’s V8 for JavaScript, rely on dynamic profiling val lines = FileReader(file) as their key mechanism to guide optimizations. While these JIT val schema = lines.next.split(",") compilers offer good average performance, their behavior is a black while (lines.hasNext) { box and the achieved performance is highly unpredictable. val fields = lines.next().split(",") In this paper, we propose to turn JIT compilation into a preci- val rec = new Record(fields,schema) sion tool by adding two essential and generic metaprogramming yld(rec) } facilities: First, allow programs to invoke JIT compilation explic- } itly. This enables controlled specialization of arbitrary code at run- time, in the style of partial evaluation. It also enables the JIT com- Figure 1. Example: Processing CSV files. piler to report warnings and errors to the program when it is un- 1. Introduction able to compile a code path in the demanded way. Second, allow the JIT compiler to call back into the program to perform compile- Just-in-time (JIT) compilation of running programs presents more time computation.
    [Show full text]
  • Csc 553 Principles of Compilation 2 : Interpreters Department Of
    Compiler tokens source Lexer Parser CSc 553 AST Principles of Compilation IR AST VM Code Interm. Semantic Gen Code Gen Analyser 2 : Interpreters VM Department of Computer Science Interpreter University of Arizona Get Next Execute Instruction Instruction [email protected] Copyright c 2011 Christian Collberg Kinds of Interpreters An interpreter is like a CPU, only in software. The compiler generates virtual machine (VM) code rather than native machine code. "APL/Prolog−style (load−and−go/interactive) interpreter" The interpreter executes VM instructions rather than native machine code. Read, lex, read Source parse, VM eval Code Code print Interpreters are semantics slow Often 10–100 times slower than executing machine Interpret code directly. portable The virtual machine code is not tied to any particular Read, lex, Source VM parse, CodeFile.vm architecture. Code Code semantics Interpreters work well with read Read very high-level, dynamic languages CodeFile.vm eval (APL,Prolog,ICON) where a lot is unknown at VM print compile-time (array bounds, etc). "Java−style interpreter" Interpret Kinds of Interpreters. Actions in an Interpreter Just−In−Time (JIT,Dynamic) Compiler Read, lex, Source VM parse, CodeFile.vm Internally, an interpreter consists of Code Code semantics 1 The interpreter engine, which executes the VM instructions. 2 Memory for storing user data. Often separated as a heap and a stack. Native 3 A stream of VM instructions. CodeFile.vm Compile Execute Code Actions in an Interpreter. Stack-Based Instruction Sets Instruct− ionstream add I := Get next instruction. Many virtual machine instruction sets (e.g. Java bytecode, store Forth) are stack based.
    [Show full text]
  • Translation and Evaluation
    Translation and evaluation A mental model for compile-time metaprogramming Document Number: P0992r0 Audience: SG7 Date: Apr 2, 2018 Reply-to: Andrew Sutton <[email protected]> The purpose of this note is to document my understanding of what it means to evaluate an expression at compile time. During my work on reflection and metaprogramming, I’ve often found myself getting lost in the various interactions between the compiler (or translator) and the evaluation subsystem. In particular, it is not always obvious what information is allowed to flow from the translation to evaluation and back. Value-based reflection and metaprogramming also add new wrinkles to the language, since they move both values and code in between translation and evaluation. Constructing a sound mental model early on is useful. This note should be helpful for anybody writing metaprogramming proposals. We begin by defining two terms: • Translation is a process concerned with lexical, syntactic, and semantic analysis of source code. The state of a translation maintains data related to the interpretation of text as programs, and includes tokens, ASTs, scopes, lookup tables, etc. • Evaluation (or execution) is a process concerned with computation. The state of an evaluation maintains data related to computation, and contains instruction streams, runtime stores, call stacks, etc. In the conventional use of C++, these two processes are physically separate: You compile a program, and then later you run the program. You cannot, during program execution, request the instantiation of a template. The translation process is no longer active at the time of execution. (This is not C#.) The constant expression facility allows a translation to request the evaluation of an expression at particular points in the program.
    [Show full text]
  • Compile-Time RTL Interpreters
    Compile-Time RTL Interpreters Sahand Kashani James R. Larus EPFL EPFL Switzerland Switzerland ABSTRACT encode the precise timing and physical structure of a circuit. Com- There is no doubt that high-level synthesis (HLS) tools have greatly pilers must preserve the semantics of their input programs, but eased accelerator development. However, many accelerators have pipelining changes the input-to-output latency of a circuit and thus already been written in a RTL language and cannot benefit from its original timing. the automated techniques commonly used by HLS tools. Pipelining Retiming is a transparent transformation that decreases a cir- is one such technique and must painstakingly be performed by a cuit’s clock period by moving existing registers around to balance human when a RTL accelerator needs to be migrated to a higher- delays. Current FPGA toolchains perform retiming under the hood, speed FPGA for example. This paper provides a first step to bringing but its applicability highly depends on the structure of the input HLS-like automated pipelining directly to accelerators written in a circuit. For example, Intel’s HyperFlex FPGA architecture has first- RTL language. class support for retiming by including bypassable registers on every routing segment of the device to help balance long routing 1 INTRODUCTION paths. However, these special registers do not support asynchro- Pipelining is the main mechanism for creating high-frequency ac- nous resets, clock enables, or initial values. Design registers which celerators. All designs—written directly in a RTL language or gen- use such features can therefore not be retimed to a routing segment erated through HLS—exhibit some form of pipelined structure to register to decrease the clock period.
    [Show full text]
  • (Just-In-Time) Compile-Time] MOP for Advanced Dispatching
    ALIA4J’s [(Just-In-Time) Compile-Time] MOP for Advanced Dispatching Christoph Bockisch Andreas Sewe Martin Zandberg Software Engineering group CASED Software Engineering group University of Twente Technische Universität Darmstadt, University of Twente Enschede, The Netherlands Gernamy Enschede, The Netherlands [email protected] [email protected] [email protected] Abstract In this paper, we propose such an approach for languages based The ALIA4J approach provides a framework for implementing ex- on dispatching, a language feature that has seen much experimen- ecution environments with support for advanced dispatching as tation by language designers in recent years, ranging from pred- found, e.g., in aspect-oriented or predicate dispatching languages. It icate dispatching [7] over aspect-oriented programming [12] and context-oriented programming [10] to various domain-specific lan- also defines an extensible meta-model acting as intermediate repre- 1 sentation for dispatching declarations, e.g., pointcut-advice or pred- guages. The ALIA4J project therefore provides an approach for icate methods. From the intermediate representation of all dispatch implementing such programming languages. As demonstrated in declarations in the program, the framework derives an execution our previous work [5], advanced dispatching is a mechanism that model for which ALIA4J specifies an generic execution strategy. subsumes the various styles of dispatching mentioned above. The meta-object protocol (MOP) formed by the meta-model and To make advanced dispatching implementable in a flexible fash- the framework are defined such that new programming language ion, we employ a meta-object protocol (MOP) together with a concepts can be implemented modularly.
    [Show full text]