This paper presents a logic based approach to debugging Java programs. In contrast with traditional debugging we propose a debugging methodology for Java programs using logical queries on individual execution states and also over the history of execution. These queries were arrived at by a systematic study of errors in object-oriented programs in our earlier research. We represent the salient events during the execution of a Java program by a logic database, and implement the queries as logic programs. Such an approach allows us to answer a number of useful and interesting queries about a Java program, such as the calling sequence that results in a certain outcome, the state of an object at a particular execution point, etc. Our system also provides the ability to compose new queries during a debugging session. We believe that logic programming offers a significant contribution to the art of object-oriented programs debugging.
JIVE's @cite_1 @cite_9 (Java Interactive Visualization Engine) design is based on the following seven criteria: (1) depict objects as environment of method execution; (2) display object states at different levels of granularity; (3) provide a sequence diagram to capture the history of execution; (4) support forwards and backwards execution of programs; (5) support queries on the runtime state; (6) produce clear and legible drawings; (7) uses exiting Java technologies. JIVE interacts with the JPDA to extract program trace. An on-line dynamic trace analysis is applied while the program runs for the first time in the forwards direction and postmortem trace analysis is applied in the backwards direction or in the forwards direction once program terminates.
@cite_9 @cite_1
2002691587 2033526593
A novel approach to the runtime visualization and analysis of object-oriented programs is presented and illustrated through a prototype system called JIVE: Java Interactive Visualization Environment. The main contributions of JIVE are: multiple concurrent representations of program state and execution history; support for forward and reverse execution; and graphical queries over program execution. This model facilitates program understanding and interactive debugging. Our visualization of runtime states clarifies the important point that objects are environments of execution. The history of object interaction is displayed via sequence diagrams, and in this way we help close the loop between design-time and run-time representations. Interactive execution is made possible by maintaining a runtime history database, which may be queried for information on variable behavior, method executions, and object interactions. We illustrate the capabilities of this system through examples. JIVE is implemented using the Java Platform Debugger Architecture and supports the Java language and libraries, including multithreaded and GUI applications. We describe a novel approach to runtime visualization of object-oriented programs. Our approach features: visualizations of execution state and history; forward and reverse execution; interactive queries during program execution; and advanced drawing capabilities involving a combination of compile-time and runtime-analysis. Our methodology is realized in a software tool called JIVE, for Java Interactive Visualization Environment. Proof, verification and analysis methods for termination all rely on two induction principles: (1) a variant function or induction on data ensuring progress towards the end and (2) some form of induction on the program structure. The abstract interpretation design principle is first illustrated for the design of new forward and backward proof, verification and analysis methods for safety. The safety collecting semantics defining the strongest safety property of programs is first expressed in a constructive fixpoint form. Safety proof and checking verification methods then immediately follow by fixpoint induction. Static analysis of abstract safety properties such as invariance are constructively designed by fixpoint abstraction (or approximation) to (automatically) infer safety properties. So far, no such clear design principle did exist for termination so that the existing approaches are scattered and largely not comparable with each other. For (1), we show that this design principle applies equally well to potential and definite termination. The trace-based termination collecting semantics is given a fixpoint definition. Its abstraction yields a fixpoint definition of the best variant function. By further abstraction of this best variant function, we derive the Floyd Turing termination proof method as well as new static analysis methods to effectively compute approximations of this best variant function. For (2), we introduce a generalization of the syntactic notion of struc- tural induction (as found in Hoare logic) into a semantic structural induction based on the new semantic concept of inductive trace cover covering execution traces by segments, a new basis for formulating program properties. Its abstractions allow for generalized recursive proof, verification and static analysis methods by induction on both program structure, control, and data. Examples of particular instances include Floyd's handling of loop cutpoints as well as nested loops, Burstall's intermittent assertion total correctness proof method, and Podelski-Rybalchenko transition invariants. Preface. 1 Introduction. 1.1 Autonomous Agents. 1.2 Characteristics of Agents. 1.3 Multi-Agent Systems. 1.4 Hello World! 2 The BDI Agent Model. 2.1 Agent-Oriented Programming. 2.2 Practical Reasoning. 2.3 A Computational Model of BDI Practical Reasoning. 2.4 The Procedural Reasoning System. 2.5 Agent Communication. 3 The Jason Agent Programming Language. 3.1 Beliefs. 3.2 Goals. 3.3 Plans. 3.4 Example: A Complete Agent Program. 3.5 Exercises. 4 Jason Interpreter. 4.1 The Reasoning Cycle. 4.2 Plan Failure. 4.3 Interpreter Configuration and Execution Modes. 4.4 Pre-Defined Plan Annotations. 4.5 Exercises. 5 Environments. 5.1 Support for Defining Simulated Environments. 5.2 Example: Running a System of Multiple Situated Agents. 5.3 Exercises. 6 Communication and Interaction. 6.1 Available Performatives. 6.2 Informal Semantics of Receiving Messages. 6.3 Example: Contract Net Protocol. 6.4 Exercises. 7 User-Defined Components. 7.1 Defining New Internal Actions. 7.2 Customising the Agent Class. 7.3 Customising the Overall Architecture. 7.4 Customising the Belief Base. 7.5 Pre-Processing Directives. 7.6 Exercises. 8 Advanced Goal-Based Programming. 8.1 BDI Programming. 8.2 Declarative (Achievement) Goal Patterns. 8.3 Commitment Strategy Patterns. 8.4 Other Useful Patterns. 8.5 Pre-Processing Directives for Plan Patterns. 9 Case Studies. 9.1 Case Study I: Gold Miners. 9.2 Case Study II: Electronic Bookstore. 10 Formal Semantics. 10.1 Semantic Rules. 10.2 Semantics of Message Exchange in a Multi-Agent System. 10.3 Semantic Rules for Receiving Messages. 10.4 Semantics of the BDI Modalities for AgentSpeak. 11 Conclusions. 11.1 Jason and Agent-Oriented Programming. 11.2 Ongoing Work and Related Research. 11.3 General Advice on Programming Style and Practice. A Reference Guide. A.1 EBNF for the Agent Language. A.2 EBNF for the Multi-Agent Systems Language. A.3 Standard Internal Actions. A.4 Pre-Defined Annotations. A.5 Pre-Processing Directives. A.6 Interpreter Configuration. Bibliography. We present a novel approach to proving the absence of timing channels. The idea is to partition the programâ s execution traces in such a way that each partition component is checked for timing attack resilience by a time complexity analysis and that per-component resilience implies the resilience of the whole program. We construct a partition by splitting the program traces at secret-independent branches. This ensures that any pair of traces with the same public input has a component containing both traces. Crucially, the per-component checks can be normal safety properties expressed in terms of a single execution. Our approach is thus in contrast to prior approaches, such as self-composition, that aim to reason about multiple (kâ � 2) executions at once. We formalize the above as an approach called quotient partitioning, generalized to any k-safety property, and prove it to be sound. A key feature of our approach is a demand-driven partitioning strategy that uses a regex-like notion called trails to identify sets of execution traces, particularly those influenced by tainted (or secret) data. We have applied our technique in a prototype implementation tool called Blazer, based on WALA, PPL, and the brics automaton library. We have proved timing-channel freedom of (or synthesized an attack specification for) 24 programs written in Java bytecode, including 6 classic examples from the literature and 6 examples extracted from the DARPA STAC challenge problems. The property of locality in program behavior has been studied and modelled extensively because of its application to memory design, code optimization, multiprogramming etc. We propose a k order Markov chain based scheme to model the sequence of time intervals between successive references to the same address in memory during program execution. Each unique address in a program is modelled separately. To validate our model, which we call the Inter-Reference Gap (IRG) model, we show substantial improvements in three different areas where it is applied. (1) We improve upon the miss ratio for the Least Recently Used (LRU) memory replacement algorithm by up to 37 . (2) We achieve up to 22 space-time product improvement over the Working Set (WS) algorithm for dynamic memory management. (3) A new trace compression technique is proposed which compresses up to 2.5 with zero error in WS simulations and up to 3.7 error in the LRU simulations. All these results are obtained experimentally, via trace driven simulations over a wide range of cache traces, page reference traces, object traces and database traces. Instrumenting programs with code to monitor runtime behavior is a common technique for profiling and debugging. In practice, instrumentation is either inserted manually by programmers, or automatically by specialized tools that monitor particular properties. We propose Program Trace Query Language (PTQL), a language based on relational queries over program traces, in which programmers can write expressive, declarative queries about program behavior. We also describe our compiler, Partiqle . Given a PTQL query and a Java program, Partiqle instruments the program to execute the query on-line. We apply several PTQL queries to a set of benchmark programs, including the Apache Tomcat Web server. Our queries reveal significant performance bugs in the jack SpecJVM98 benchmark, in Tomcat, and in the IBM Java class library, as well as some correct though uncomfortably subtle code in the Xerces XML parser. We present performance measurements demonstrating that our prototype system has usable performance. In this paper, we study the problem of automatically finding program executions that reach a particular target line. This problem arises in many debugging scenarios; for example, a developer may want to confirm that a bug reported by a static analysis tool on a particular line is a true positive. We propose two new directed symbolic execution strategies that aim to solve this problem: shortest-distance symbolic execution (SDSE) uses a distance metric in an interprocedural control flow graph to guide symbolic execution toward a particular target; and call-chain-backward symbolic execution (CCBSE) iteratively runs forward symbolic execution, starting in the function containing the target line, and then jumping backward up the call chain until it finds a feasible path from the start of the program. We also propose a hybrid strategy, Mix-CCBSE, which alternates CCBSE with another (forward) search strategy. We compare these three with several existing strategies from the literature on a suite of six GNU Coreutils programs. We find that SDSE performs extremely well in many cases but may fail badly. CCBSE also performs quite well, but imposes additional overhead that sometimes makes it slower than SDSE. Considering all our benchmarks together, Mix-CCBSE performed best on average, combining to good effect the features of its constituent components. The author introduces PROVIDE, a source-level process visualization and debugging environment currently under development at the University of Illinois at Chicago. PROVIDE is a modern coding and debugging environment that is designed to allow the user to configure interaction at a desired level of abstraction. It emphasizes the use of interactive computer graphics for the illustration of program execution, with special attention to the requirements of program debugging. The major features of PROVIDE are presented, especially the concepts of deferred-binding program animation, which allows users to interactively change the depiction of program execution during the debugging task, and process history consistency maintenance, which guarantees a consistent (automatically updated) record of program execution in the face of changes to program instructions and run-time data values. The current PROVIDE prototype is implemented on Macintosh workstations networked to a VAX 11 780 running 4.2 BSD Unix. > Quality assurance for highly-configurable systems is challenging due to the exponentially growing configuration space. Interactions among multiple options can lead to surprising behaviors, bugs, and security vulnerabilities. Analyzing all configurations systematically might be possible though if most options do not interact or interactions follow specific patterns that can be exploited by analysis tools. To better understand interactions in practice, we analyze program traces to characterize and identify where interactions occur on control flow and data. To this end, we developed a dynamic analysis for Java based on variability-aware execution and monitor executions of multiple small to medium-sized programs. We find that the essential configuration complexity of these programs is indeed much lower than the combinatorial explosion of the configuration space indicates. However, we also discover that the interaction characteristics that allow scalable and complete analyses are more nuanced than what is exploited by existing state-of-the-art quality assurance strategies. COSTA is a static analyzer for Java bytecode which is able to infer cost and termination information for large classes of programs. The analyzer takes as input a program and a resource of interest, in the form of a cost model, and aims at obtaining an upper bound on the execution cost with respect to the resource and at proving program termination. The costa system has reached a considerable degree of maturity in that (1) it includes state-of-the-art techniques for statically estimating the resource consumption and the termination behavior of programs, plus a number of specialized techniques which are required for achieving accurate results in the context of object-oriented programs, such as handling numeric fields in value analysis; (2) it provides several nontrivial notions of cost (resource consumption) including, in addition to the number of execution steps, the amount of memory allocated in the heap or the number of calls to some user-specified method; (3) it provides several user interfaces: a classical command line, a Web interface which allows experimenting remotely with the system without the need of installing it locally, and a recently developed Eclipse plugin which facilitates the usage of the analyzer, even during the development phase; (4) it can deal with both the Standard and Micro editions of Java. In the tool demonstration, we will show that costa is able to produce meaningful results for non-trivial programs, possibly using Java libraries. Such results can then be used in many applications, including program development, resource usage certification, program optimization, etc. In this paper, we compose six di erent Python and Prolog VMs into 4 pairwise compositions: one using C interpreters; one running on the JVM; one using meta-tracing interpreters; and one using a C interpreter and a meta-tracing interpreter. We show that programs that cross the language barrier frequently execute faster in a meta-tracing composition, and that meta-tracing imposes a significantly lower overhead on composed programs relative to mono-language programs. 1. Overview Programming language composition aims to allow the mixing of programming languages in a fine-grained manner. This vision brings many challenging problems, from the interaction of language semantics to performance. In this paper, we investigate the runtime performance of composed programs in high-level languages. We start from the assumption that execution of such programs is most likely to be through composing language implementations that use interpreters and VMs, rather than traditional compilers. This raises the question: how do di erent styles of composition a ect performance? Clearly, such a question cannot have a single answer but, to the best of our knowledge, this issue has not been explored in the past. This paper's hypothesis is that meta-tracing - a relatively new technique used to produce JIT (Just-In-Time) compilers from interpreters (1) - will lead to faster interpreter composition than traditional approaches. To test this hypothesis, we present a Python and Prolog composition which allows Python programs to embed and call Prolog programs. We then implement the composition in four di erent ways, comparing the absolute times and the relative cross-language costs of each. In addition to the 'traditional' approaches to composing interpreters (in C and upon the JVM), we also investigate the application of meta-tracing to interpreter composition. The experiments we then carry out confirm our initial hypothesis. There is a long tradition of composing Prolog with other languages (with e.g. Icon (2), Lisp (3), and Smalltalk (4)) because one can express certain types of programs far easier in Prolog than in other languages. We have two additional reasons for choosing this pairing. First, these languages represent very di erent points in the language design space: Prolog's inherently Optimizing programs at run-time provides opportunities to apply aggressive optimizations to programs based on information that was not available at compile time. At run time, programs can be adapted to better exploit architectural features, optimize the use of dynamic libraries, and simplify code based on run-time constants. Our profiling system provides a framework for collecting information required for performing run-time optimization. We sample the performance hardware registers available on an Itanium processor, and select a set of code that is likely to lead to important performance-events. We gather distribution information about the performance-events we wish to monitor, and test our traces by estimating the ability for dynamic patching of a program to execute run-time generated traces. Our results show that we are able to capture 58 of execution time across various SPEC2000 integer benchmarks using our profile and patching techniques on a relatively small number of frequently executed execution paths. Our profiling and detection system overhead increases execution time by only 2-4 . This paper presents K-Java, a complete executable formal semantics of Java 1.4. K-Java was extensively tested with a test suite developed alongside the project, following the Test Driven Development methodology. In order to maintain clarity while handling the great size of Java, the semantics was split into two separate definitions -- a static semantics and a dynamic semantics. The output of the static semantics is a preprocessed Java program, which is passed as input to the dynamic semantics for execution. The preprocessed program is a valid Java program, which uses a subset of the features of Java. The semantics is applied to model-check multi-threaded programs. Both the test suite and the static semantics are generic and ready to be used in other Java-related projects. Over the last seven years, we have developed static-analysis methods to recover a good approximation to the variables and dynamically allocated memory objects of a stripped executable, and to track the flow of values through them. The article presents the algorithms that we developed, explains how they are used to recover Intermediate Representations (IRs) from executables that are similar to the IRs that would be available if one started from source code, and describes their application in the context of program understanding and automated bug hunting. Unlike algorithms for analyzing executables that existed prior to our work, the ones presented in this article provide useful information about memory accesses, even in the absence of debugging information. The ideas described in the article are incorporated in a tool for analyzing Intel x86 executables, called CodeSurfer x86. CodeSurfer x86 builds a system dependence graph for the program, and provides a GUI for exploring the graph by (i) navigating its edges, and (ii) invoking operations, such as forward slicing, backward slicing, and chopping, to discover how parts of the program can impact other parts. To assess the usefulness of the IRs recovered by CodeSurfer x86 in the context of automated bug hunting, we built a tool on top of CodeSurfer x86, called Device-Driver Analyzer for x86 (DDA x86), which analyzes device-driver executables for bugs. Without the benefit of either source code or symbol-table debugging information, DDA x86 was able to find known bugs (that had been discovered previously by source-code analysis tools), along with useful error traces, while having a low false-positive rate. DDA x86 is the first known application of program analysis verification techniques to industrial executables. In this paper we devise new paging heuristics motivated by the access graph model of paging. Unlike the access graph model and the related Markov paging model our heuristics are truly on-line in that we do not assume any prior knowledge of the program just about to be executed. The Least Recently Used heuristic for paging is remarkably good, and is known experimentally to be superior to many of the suggested alternatives on real program traces. Experiments we ve performed suggest that our heuristics beat LRU fairly consistently, over a wide range of cache sizes and programs. The number of page faults can be as low as 1 2 the number of page faults for LRU and is on average 7 to 9 percent less than the number of faults for LRU. (Depending on how this average is computed.) We have built a program tracer that gives the page access sequence for real program executions of 200 - 3,300 thousand page access requests, and our simulations are based on 25 of these program traces. Overall, we have performed several thousand such simulations. While we have no real evidence to suggest that the programs we ve traced are typical in any sense, we havemore » made use of an experimental open_quotes protocol close_quotes designed to avoid experimenter bias. We strongly emphasize that our results are only preliminary and that much further work needs to be done.« less We introduce Elephant Tracks (ET), a dynamic program analysis tool for producing traces of garbage collection events, including object allocations, object deaths, and pointer updates. Like prior work, our tracing tool is based on the Merlin algorithm [2002], but offers several substantial new capabilities. First, it is much more precise than previous tools: it measures time in terms of method entries and exits, allowing it to place events precisely in the context of the program structure. Furthermore, we exploit this notion of time to actually reduce the amount of work required to compute precise object death times. Second, it is implemented using a combination of JVM Tool Interface (JVMTI) Microsystems [2004] callbacks and bytecode rewriting, and works with any standard JVM. Finally, it properly handles Java Reference objects, including weak references, finalization, and multithreaded programs. We describe the design of jRapture: a tool for capturing and replaying Java program executions in the field. jRapture works with Java binaries (byte code) and any compliant implementation of the Java virtual machine. It employs a lightweight, transparent capture process that permits unobtrusive capture of a Java programs executions. jRapture captures interactions between a Java program and the system, including GUI, file, and console inputs, among other types, and on replay it presents each thread with exactly the same input sequence it saw during capture. In addition, jRapture has a profiling interface that permits a Java program to be instrumented for profiling o after its executions have been captured. Using an XML-based profiling specification language a tester can specify various forms of profiling to be carried out during replay. A conventional query execution engine in a database system essentially uses a SQL virtual machine (SVM) to interpret a dataflow tree in which each node is associated with a relational operator. During query evaluation, a single tuple at a time is processed and passed among the operators. Such a model is popular because of its efficiency for pipelined processing. However, since each operator is implemented statically, it has to be very generic in order to deal with all possible queries. Such generality tends to introduce significant runtime inefficiency, especially in the context of memory-resident systems, because the granularity of data commercial system, using SVM. processing (a tuple) is too small compared with the associated overhead. Another disadvantage in such an engine is that each operator code is compiled statically, so query-specific optimization cannot be applied. To improve runtime efficiency, we propose a compiled execution engine, which, for a given query, generates new query-specific code on the fly, and then dynamically compiles and executes the code. The Java platform makes our approach particularly interesting for several reasons: (1) modern Java Virtual Machines (JVM) have Just- In-Time (JIT) compilers that optimize code at runtime based on the execution pattern, a key feature that SVMs lack; (2) because of Javas continued popularity, JVMs keep improving at a faster pace than SVMs, allowing us to exploit new advances in the Java runtime in the future; (3) Java is a dynamic language, which makes it convenient to load a piece of new code on the fly. In this paper, we develop both an interpreted and a compiled query execution engine in a relational, Java-based, in-memory database prototype, and perform an experimental study. Our experimental results on the TPC-H data set show that, despite both engines benefiting from JIT, the compiled engine runs on average about twice as fast as the interpreted one, and significantly faster than an in-memory 1. Introduction and Overview.- 1.1 Why Integrate Neurons and Symbols?.- 1.2 Strategies of Neural-Symbolic Integration.- 1.3 Neural-Symbolic Learning Systems.- 1.4 A Simple Example.- 1.5 How to Read this Book.- 1.6 Summary.- 2. Background.- 2.1 General Preliminaries.- 2.2 Inductive Learning.- 2.3 Neural Networks.- 2.3.1 Architectures.- 2.3.2 Learning Strategy.- 2.3.3 Recurrent Networks.- 2.4 Logic Programming.- 2.4.1 What is Logic Programming?.- 2.4.2 Fixpoints and Definite Programs.- 2.5 Nonmonotonic Reasoning.- 2.5.1 Stable Models and Acceptable Programs.- 2.6 Belief Revision.- 2.6.1 Truth Maintenance Systems.- 2.6.2 Compromise Revision.- I. Knowledge Refinement in Neural Networks.- 3. Theory Refinement in Neural Networks.- 3.1 Inserting Background Knowledge.- 3.2 Massively Parallel Deduction.- 3.3 Performing Inductive Learning.- 3.4 Adding Classical Negation.- 3.5 Adding Met alevel Priorities.- 3.6 Summary and Further Reading.- 4. Experiments on Theory Refinement.- 4.1 DNA Sequence Analysis.- 4.2 Power Systems Fault Diagnosis.- 4.3.Discussion.- 4.4.Appendix.- II. Knowledge Extraction from Neural Networks.- 5. Knowledge Extraction from Trained Networks.- 5.1 The Extraction Problem.- 5.2 The Case of Regular Networks.- 5.2.1 Positive Networks.- 5.2.2 Regular Networks.- 5.3 The General Case Extraction.- 5.3.1 Regular Subnetworks.- 5.3.2 Knowledge Extraction from Subnetworks.- 5.3.3 Assembling the Final Rule Set.- 5.4 Knowledge Representation Issues.- 5.5 Summary and Further Reading.- 6. Experiments on Knowledge Extraction.- 6.1 Implementation.- 6.2 The Monk's Problems.- 6.3 DNA Sequence Analysis.- 6.4 Power Systems Fault Diagnosis.- 6.5 Discussion.- III. Knowledge Revision in Neural Networks.- 7. Handling Inconsistencies in Neural Networks.- 7.1 Theory Revision in Neural Networks.- 7.1.1The Equivalence with Truth Maintenance Systems.- 7.1.2Minimal Learning.- 7.2 Solving Inconsistencies in Neural Networks.- 7.2.1 Compromise Revision.- 7.2.2 Foundational Revision.- 7.2.3 Nonmonotonic Theory Revision.- 7.3 Summary of the Chapter.- 8. Experiments on Handling Inconsistencies.- 8.1 Requirements Specifications Evolution as Theory Refinement.- 8.1.1Analysing Specifications.- 8.1.2Revising Specifications.- 8.2 The Automobile Cruise Control System.- 8.2.1Knowledge Insertion.- 8.2.2Knowledge Revision: Handling Inconsistencies.- 8.2.3Knowledge Extraction.- 8.3 Discussion.- 8.4 Appendix.- 9. Neural-Symbolic Integration: The Road Ahead.- 9.1 Knowledge Extraction.- 9.2 Adding Disjunctive Information.- 9.3 Extension to the First-Order Case.- 9.4 Adding Modalities.- 9.5 New Preference Relations.- 9.6 A Proof Theoretical Approach.- 9.7 The "Forbidden Zone" [Amax, Amin].- 9.8 Acceptable Programs and Neural Networks.- 9.9 Epilogue.