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Register a new userEfficient and Expressive Bytecode-Level Instrumentation for Java Programs
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Added by
Chukri Soueidi
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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We present an efficient and expressive tool for the instrumentation of Java programs at the bytecode-level. BISM (Bytecode-Level Instrumentation for Software Monitoring) is a light-weight Java bytecode instrumentation tool that features an expressive high-level control-flow-aware instrumentation language. The language is inspired by the aspect-oriented programming paradigm in modularizing instrumentation into separate transformers, that encapsulate joinpoint selection and advice inlining. BISM allows capturing joinpoints ranging from bytecode instructions to methods execution and provides comprehensive static and dynamic context information. It runs in two instrumentation modes: build-time and load-time. BISM also provides a mechanism to compose transformers and automatically detect their collision in the base program. Transformers in a composition can control the visibility of their advice and other instructions from the base program. We show several example applications for BISM and demonstrate its effectiveness using three experiments: a security scenario, a financial transaction system, and a general runtime verification case. The results show that BISM instrumentation incurs low runtime and memory overheads.
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BISM (Bytecode-Level Instrumentation for Software Monitoring) is a lightweight bytecode instrumentation tool that features an expressive high-level control-flow-aware instrumentation language. The language follows the aspect-oriented programming paradigm by adopting the joinpoint model, advice inlining, and separate instrumentation mechanisms. BISM provides joinpoints ranging from bytecode instruction to method execution, access to comprehensive static and dynamic context information, and instrumentation methods. BISM runs in two instrumentation modes: build-time and load-time. We demonstrate BISM effectiveness using two experiments: a security scenario and a general runtime verification case. The results show that BISM instrumentation incurs low runtime and memory overheads.
We introduce a fully automated static analysis that takes a sequential Java bytecode program P as input and attempts to prove that there exists an infinite execution of P. The technique consists in compiling P into a constraint logic program P_CLP and in proving non-termination of P_CLP; when P consists of instructions that are exactly compiled into constraints, the non-termination of P_CLP entails that of P. Our approach can handle method calls; to the best of our knowledge, it is the first static approach for Java bytecode able to prove the existence of infinite recursions. We have implemented our technique inside the Julia analyser. We have compared the results of Julia on a set of 113 programs with those provided by AProVE and Invel, the only freely usable non-termination analysers comparable to ours that we are aware of. Only Julia could detect non-termination due to infinite recursion.
A software analysis is a computer program that takes some representation of a software product as input and produces some useful information about that product as output. A software product line encompasses emph{many} software product variants, and thus existing analyses can be applied to each of the product variations individually, but not to the entire product line as a whole. Enumerating all product variants and analyzing them one by one is usually intractable due to the combinatorial explosion of the number of product variants with respect to product line features. Several software analyses (e.g., type checkers, model checkers, data flow analyses) have been redesigned/re-implemented to support variability. This usually requires a lot of time and effort, and the variability-aware version of the analysis might have new errors/bugs that do not exist in the original one. Given an analysis program written in a functional language based on PCF, in this paper we present two approaches to transforming (lifting) it into a semantically equivalent variability-aware analysis. A light-weight approach (referred to as emph{shallow lifting}) wraps the analysis program into a variability-aware version, exploring all combinations of its input arguments. Deep lifting, on the other hand, is a program rewriting mechanism where the syntactic constructs of the input program are rewritten into their variability-aware counterparts. Compositionally this results in an efficient program semantically equivalent to the input program, modulo variability. We present the correctness criteria for functional program lifting, together with correctness proof sketches of our program transformations. We evaluate our approach on a set of program analyses applied to the BusyBox C-language product line.
Motivated by the fast adoption of WebAssembly, we propose the first functional pipeline to support the superoptimization of WebAssembly bytecode. Our pipeline works over LLVM and Souper. We evaluate our superoptimization pipeline with 12 programs from the Rosetta code project. Our pipeline improves the code section size of 8 out of 12 programs. We discuss the challenges faced in superoptimization of WebAssembly with two case studies.
This paper presents a comparative study to evaluate and compare Fortran with the two most popular programming languages Java and C++. Fortran has gone through major and minor extensions in the years 2003 and 2008. (1) How much have these extensions made Fortran comparable to Java and C++? (2) What are the differences and similarities, in supporting features like: Templates, object constructors and destructors, abstract data types and dynamic binding? These are the main questions we are trying to answer in this study. An object-oriented ray tracing application is implemented in these three languages to compare them. By using only one program we ensured there was only one set of requirements thus making the comparison homogeneous. Based on our literature survey this is the first study carried out to compare these languages by applying software metrics to the ray tracing application and comparing these results with the similarities and differences found in practice. We motivate the language implementers and compiler developers, by providing binary analysis and profiling of the application, to improve Fortran object handling and processing, and hence making it more prolific and general. This study facilitates and encourages the reader to further explore, study and use these languages more effectively and productively, especially Fortran.
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