Görel Hedin

Bio: Görel Hedin is an academic researcher from Lund University. The author has contributed to research in topics: Compiler & L-attributed grammar. The author has an hindex of 23, co-authored 102 publications receiving 2289 citations.

The jastadd extensible java compiler

Abstract: The JastAdd Extensible Java Compiler is a high quality Java compiler that is easy to extend in order to build static analysis tools for Java, and to extend Java with new language constructs. It is built modularly, with a Java 1.4 compiler that is extended to a Java 5 compiler. Example applications that are built as extensions include an alternative backend that generates Jimple, an extension of Java with AspectJ constructs, and the implementation of a pluggable type system for non-null checking and inferenc. The system is implemented using JastAdd, a declarative Java-like language. We describe the compiler architecture, the major design ideas for building and extending the compiler, in particular, for dealing with complex extensions that affect name and type analysis. Our extensible compiler compares very favorably concerning quality, speed and size with other extensible Java compiler frameworks. It also compares favorably in quality and size compared with traditional non-extensible Java compilers, and it runs within a factor of three compared to javac.

Rewritable Reference Attributed Grammars

Abstract: This paper presents an object-oriented technique for rewriting abstract syntax trees in order to simplify compilation. The technique, Rewritable Reference Attributed Grammars (ReRAGs), is completely declarative and supports both rewrites and computations by means of attributes. We have implemented ReRAGs in our aspect-oriented compiler compiler tool JastAdd II. Our largest application is a complete static-semantic analyzer for Java 1.4. ReRAGs uses three synergistic mechanisms for supporting separation of concerns: inheritance for model modularization, aspects for cross-cutting concerns, and rewrites that allow computations to be expressed on the most suitable model. This allows compilers to be written in a high-level declarative and modular fashion, supporting language extensibility as well as reuse of modules for different compiler-related tools. We present the ReRAG formalism, its evaluation algorithm, and examples of its use. Initial measurements using a subset of the Java class library as our benchmarks indicate that our generated compiler is only a few times slower than the standard compiler, javac, in J2SE 1.4.2 SDK. This shows that ReRAGs are already useful for large-scale practical applications, despite that optimization has not been our primary concern so far.

Reference Attributed Grammars

Abstract: An object-oriented extension to canonical attribute grammars is described, permitting attributes to be references to arbitrary nodes in the syntax tree, and attributes to be accessed via the reference attributes. Important practical problems such as name and type analysis for object-oriented languages can be expressed in a concise and modular manner in these grammars, and an optimal evaluation algorithm is available. An extensive example is given, capturing all the key constructs in object-oriented languages including block structure, classes, inheritance, qualified use, and assignment compatibility in the presence of subtyping. The formalism and algorithm have been implemented in APPLAB, an interactive language development tool.

JastAdd: an aspect-oriented compiler construction system

Abstract: We describe JastAdd, a Java-based system for compiler construction. JastAdd is centered around an object-oriented representation of the abstract syntax tree where reference variables can be used to link together different parts of the tree. JastAdd supports the combination of declarative techniques (using Reference Attributed Grammars) and imperative techniques (using ordinary Java code) in implementing the compiler. The behavior can be modularized into different aspects, e.g. name analysis, type checking, code generation, etc., that are woven together into classes using aspect-oriented programming techniques, providing a safer and more powerful alternative to the Visitor pattern. The JastAdd system is independent of the underlying parsing technology and supports any noncircular dependencies between computations, thereby allowing general multi-pass compilation. The attribute evaluator (optimal recursive evaluation) is implemented very conveniently using Java classes, interfaces, and virtual methods.

An overview of AspectJ

Abstract: Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

Test Driven Development: By Example

Abstract: From the Book: “Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons: Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). In Test-Driven Development, you: Write new code only if you first have a failing automated test.Eliminate duplication. Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy The two rules imply an order to the tasks ofprogramming: 1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first 2. Green—make the test work quickly, committing whatever sins necessary in the process 3. Refactor—eliminate all the duplication created in just getting the test to work Red/green/refactor. The TDD’s mantra. Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications: If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage. Courage Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects: Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and: Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet. The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test. Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately. That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress. There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code. Once you are finished reading this book, you should be ready to: Start simplyWrite automated testsRefactor to add design decisions one at a time This book is organized into three sections. An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples. I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference. Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read. A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

The spec# programming system: an overview

Abstract: The Spec# programming system is a new attempt at a more cost effective way to develop and maintain high-quality software. This paper describes the goals and architecture of the Spec# programming system, consisting of the object-oriented Spec# programming language, the Spec# compiler, and the Boogie static program verifier. The language includes constructs for writing specifications that capture programmer intentions about how methods and data are to be used, the compiler emits run-time checks to enforce these specifications, and the verifier can check the consistency between a program and its specifications.