Hridesh Rajan

Bio: Hridesh Rajan is an academic researcher from Iowa State University. The author has contributed to research in topics: Concurrency & AspectJ. The author has an hindex of 26, co-authored 164 publications receiving 2928 citations. Previous affiliations of Hridesh Rajan include University of Virginia.

Boa: a language and infrastructure for analyzing ultra-large-scale software repositories

Abstract: In today's software-centric world, ultra-large-scale software repositories, e.g. SourceForge (350,000+ projects), GitHub (250,000+ projects), and Google Code (250,000+ projects) are the new library of Alexandria. They contain an enormous corpus of software and information about software. Scientists and engineers alike are interested in analyzing this wealth of information both for curiosity as well as for testing important hypotheses. However, systematic extraction of relevant data from these repositories and analysis of such data for testing hypotheses is hard, and best left for mining software repository (MSR) experts! The goal of Boa, a domain-specific language and infrastructure described here, is to ease testing MSR-related hypotheses. We have implemented Boa and provide a web-based interface to Boa's infrastructure. Our evaluation demonstrates that Boa substantially reduces programming efforts, thus lowering the barrier to entry. We also see drastic improvements in scalability. Last but not least, reproducing an experiment conducted using Boa is just a matter of re-running small Boa programs provided by previous researchers.

Modular software design with crosscutting interfaces

Abstract: Aspect-oriented programming (AOP) languages such as AspectJ offer new mechanisms and possibilities for decomposing systems into modules and composing modules into systems. The key mechanism in AspectJ is the advising of crosscutting sets of join points. An aspect module uses a pointcut descriptor (PCD) to declaratively specify sets of points in program executions. Our approach employs crosscut programming interfaces, or XPIs. XPIs are explicit, abstract interfaces that decouple aspects from details of advised code

Information hiding interfaces for aspect-oriented design

Abstract: The growing popularity of aspect-oriented languages, such as AspectJ, and of corresponding design approaches, makes it important to learn how best to modularize programs in which aspect-oriented composition mechanisms are used. We contribute an approach to information hiding modularity in programs that use quantified advising as a module composition mechanism. Our approach rests on a new kind of interface: one that abstracts a crosscutting behavior, decouples the design of code that advises such a behavior from the design of the code to be advised, and that can stipulate behavioral contracts. Our interfaces establish design rules that govern how specific points in program execution are exposed through a given join point model and how conforming code on either side should behave. In a case study of the HyperCast overlay network middleware system, including a real options analysis, we compare the widely cited oblivious design approach with our own, showing significant weaknesses in the former and benefits in the latter.

Are code examples on an online Q&A forum reliable?: a study of API misuse on stack overflow

Abstract: Programmers often consult an online Q&A forum such as Stack Overflow to learn new APIs. This paper presents an empirical study on the prevalence and severity of API misuse on Stack Overflow. To reduce manual assessment effort, we design ExampleCheck, an API usage mining framework that extracts patterns from over 380K Java repositories on GitHub and subsequently reports potential API usage violations in Stack Overflow posts. We analyze 217,818 Stack Overflow posts using ExampleCheck and find that 31% may have potential API usage violations that could produce unexpected behavior such as program crashes and resource leaks. Such API misuse is caused by three main reasons---missing control constructs, missing or incorrect order of API calls, and incorrect guard conditions. Even the posts that are accepted as correct answers or upvoted by other programmers are not necessarily more reliable than other posts in terms of API misuse. This study result calls for a new approach to augment Stack Overflow with alternative API usage details that are not typically shown in curated examples.

Ptolemy: A Language with Quantified, Typed Events

Abstract: Implicit invocation (II) and aspect-oriented (AO) languages provide related but distinct mechanisms for separation of concerns. II languages have explicitly announced events that run registered observer methods. AO languages have implicitly announced events that run method-like but more powerful advice. A limitation of II languages is their inability to refer to a large set of events succinctly. They also lack the expressive power of AO advice. Limitations of AO languages include potentially fragile dependence on syntactic structure that may hurt maintainability, and limits on the available set of implicit events and the reflective contextual information available. Quantified, typed events, as implemented in our language Ptolemy, solve all these problems. This paper describes Ptolemy and explores its advantages relative to both II and AO languages.

The Economic Institutions of Capitalism

Abstract: The 2008 crash has left all the established economic doctrines - equilibrium models, real business cycles, disequilibria models - in disarray. Part of the problem is due to Smith’s "veil of ignorance": individuals unknowingly pursue society’s interest and, as a result, have no clue as to the macroeconomic effects of their actions: witness the Keynes and Leontief multipliers, the concept of value added, fiat money, Engel’s law and technical progress, to name but a few of the macrofoundations of microeconomics. A good viewpoint to take bearings anew lies in comparing the post-Great Depression institutions with those emerging from Thatcher and Reagan’s economic policies: deregulation, exogenous vs. endoge- nous money, shadow banking vs. Volcker’s Rule. Very simply, the banks, whose lending determined deposits after Roosevelt, and were a public service became private enterprises whose deposits determine lending. These underlay the great moderation preceding 2006, and the subsequent crash.

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.