This article explores the use of compiler techniques to accomplish code compaction to yield smaller executables. The main contribution of this article is to show that careful, aggressive, interprocedural optimization, together with procedural abstraction of repeated code fragments, can yield significantly better reductions in code size than previous approaches, which have generally focused on abstraction of repeated instruction sequences. We also show how "equivalent" code fragments can be detected and factored out using conventional compiler techniques, and without having to resort to purely linear treatments of code sequences as in suffix-tree-based approaches, thereby setting up a framework for code compaction that can be more exible in its treatment of what code fragments are considered equivalent. Our ideas have been implemented in the form of a binary-rewriting tool that reduces the size of executables by about 30% on the average.

Current research in compiler optimization counts mainly CPU time and perhaps the first cache level or two. This view has been important but is becoming myopic, at least from a system-wide viewpoint, as the ratio of network and disk speeds to CPU speeds grows exponentially. For example, we have seen the CPU idle for most of the time during paging, so compressing pages can increase total performance even though the CPU must decompress or interpret the page contents. Another profile shows that many functions are called just once, so reduced paging could pay for their interpretation overhead. This paper describes:. Measurements that show how code compression can save space and total time in some important real-world scenarios.. A compressed executable representation that is roughly the same size as gzipped x86 programs and can be interpreted without decompression. It can also be compiled to high-quality machine code at 2.5 megabytes per second on a 120MHz Pentium processor l A compressed “wire ” representation that must be decompressed before execution but is, for example, roughly 21 % the size of SPARC code when compressing gee.

Historically, compilers have operated by applying a fixed set of optimizations in a predetermined order. We call such an ordered list of optimizations a compilation sequence. This paper describes a prototype system that uses biased random search to discover a program-specific compilation sequence that minimizes an explicit, external objective function. The result is a compiler framework that adapts its behavior to the application being compiled, to the pool of available transformations, to the objective function, and to the target machine.

As computers are increasingly used in contexts where the amount of available memory is limited, it becomes important to devise techniques that reduce the memory footprint of application programs while leaving them in an executable form. This paper describes an approach to applying data compression techniques to reduce the size of infrequently executed portions of a program. The compressed code is decompressed dynamically (via software) if needed, prior to execution. The use of data compression techniques increases the amount of code size reduction that can be achieved; their application to infrequently executed code limits the runtime overhead due to dynamic decompression; and the use of software decompression renders the approach generally applicable, without requiring specialized hardware. The code size reductions obtained depend on the threshold used to determine what code is "infrequently executed" and hence should be compressed: for low thresholds, we see size reductions of 13.7% to 18.8%, on average, for a set of embedded applications, without excessive runtime overhead.

In recent years there has been an increasing trend towards the incorporation of computers into a variety of devices where the amount of available memory is limited. This makes it desirable to try and reduce the size of applications where possible. This paper explores the use of compiler techniques to accomplish code compression to yield smaller executables. The main contribution of this paper is that, by showing how "equivalent" code fragments can be detected and factored out without having to resort to purely linear treatments of code sequences as in suffix-tree-based approaches, it sets up a framework for code compression that can be more flexible in its treatment of what code fragments are considered "equivalent." Our ideas have been implemented in the form of a binary-rewriting tool that is able to achieve significantly better compression than previous approaches.

Code duplication or copying a code fragment and then reuse by pasting with or without any modifications is a well known code smell in software maintenance. Several studies show that about 5 % to 20 % of a software systems can contain duplicated code, which is basically the results of copying existing code fragments and using then by pasting with or without minor modifications. One of the major shortcomings of such duplicated fragments is that if a bug is detected in a code fragment, all the other fragments similar to it should be investigated to check the possible existence of the same bug in the similar fragments. Refactoring of the duplicated code is another prime issue in software maintenance although several studies claim that refactoring of certain clones are not desirable and there is a risk of removing them. However, it is also widely agreed that clones should at least be detected. In this paper, we survey the state of the art in clone detection research. First, we describe the clone terms commonly used in the literature along with their corresponding mappings to the commonly used clone types. Second, we provide a review of the existing

Legacy code can often be made more understandable and maintainable by extracting out selected sets of statements to form procedures and replacing the extracted code with procedure calls. Sets of statements that are noncontiguous and/or include non-local jumps (caused by gotos, breaks, continues, etc.) can be difficult to extract, and usually cause previous automatic-extraction algorithms to fail or to produce poor results.

In this paper, we introduce the notion of prolific and non-prolific types, based on the number of instantiated objects of those types. We demonstrate that distinguishing between these types enables a new class of techniques for memory management and data locality, and facilitates the deployment of known techniques. Specifically, we first present a new type-based approach to garbage collection that has similar attributes but lower cost than generational collection. Then we describe the short type pointer technique for reducing memory requirements of objects (data) used by the program. We also discuss techniques to facilitate the recycling of prolific objects and to simplify object co-allocation decisions.

This paper describes the design and implementation of a method for producing compact, bytecoded instruction sets and interpreters for them. It accepts a grammar for programs written using a simple bytecoded stack-based instruction set, as well as a training set of sample programs. The system transforms the grammar, creating an expanded grammar that represents the same language as the original grammar, but permits a shorter derivation of the sample programs and others like them. A program's derivation under the expanded grammar forms the compressed bytecode representation of the program. The interpreter for this bytecode is automatically generated from the original bytecode interpreter and the expanded grammar. Programs expressed using compressed bytecode can be substantially smaller than their original bytecode representation and even their machine code representation. For example, compression cuts the bytecode for lee from 199KB to 58KB but increases the size of the interpreter by just over 11KB.

Code compression coupled with dynamic decompression is an important technique for both embedded and general-purpose microprocessors. Post-fetch decompression, in which decompression is performed after the compressed instructions have been fetched, allows the instruction cache to store compressed code but requires a highly efficient decompression implementation. We propose implementing post-fetch decompression using dynamic instruction stream editing (DISE), a programmable decoder---similar in structure to those in many IA32 processors---that is used to add functionality to an application by injecting custom code snippets into its fetched instruction stream. A DISE implementation of post-fetch decompression naturally supports customized program-specific decompression dictionaries, enables parameterized decompression allowing similar instruction sequences to share dictionary entries, and uses no decompression-specific hardware. Cycle-level simulation of DISE decompression shows that it can reduce static program size by 35% and execution time by 20%. Parameterized decompression, a feature unique to DISE, accounts for 20% of the code size reduction by making more effective use of the dictionary and allowing PC-relative branches to be included in compressed sequences. DISE-based compression can reduce total energy consumption by 10% and the energy-delay product by as much as 20%. Categories and Subject Descriptors B.3 [Hardware]: Memory Structures