Robert Dyer

Nu: a dynamic aspect-oriented intermediate language model and virtual machine for flexible runtime adaptation

A variety of dynamic aspect-oriented language constructs are proposed in recent literature with corresponding, compelling use cases. Such constructs demonstrate the need to dynamically adapt the set of join points intercepted at a fine-grained level. The notion of morphing aspects and continuous weaving is motivated by this need. We propose an intermediate language model called Nu, that extends object-oriented intermediate language models with two fine-grained deployment primitives: bind and remove. These primitives offer a higher level of abstraction as a compilation target for dynamic aspect-oriented language constructs, thereby making it easier to support such constructs. We present the design and implementation of the Nu model in the Sun Hotspot VM, an industrial strength virtual machine, which serves to show the feasibility of the intermediate language design. Our implementation uses dedicated caching mechanisms to significantly reduce the amortized costs of join point dispatch. Our evaluation shows that the cost of supporting a dynamic deployment model can be reduced to as little as ~1.5%. We demonstrate the potential utility of the intermediate language design by expressing a variety of aspect-oriented source language constructs of dynamic flavor such as CaeserJs deploy, history-based pointcuts, and control flow constructs in terms of the Nu model.

Mining preconditions of APIs in large-scale code corpus

Modern software relies on existing application programming interfaces (APIs) from libraries. Formal specifications for the APIs enable many software engineering tasks as well as help developers correctly use them. In this work, we mine large-scale repositories of existing open-source software to derive potential preconditions for API methods. Our key idea is that APIs’ preconditions would appear frequently in an ultra-large code corpus with a large number of API usages, while project-specific conditions will occur less frequently. First, we find all client methods invoking APIs. We then compute a control dependence relation from each call site and mine the potential conditions used to reach those call sites. We use these guard conditions as a starting point to automatically infer the preconditions for each API. We analyzed almost 120 million lines of code from SourceForge and Apache projects to infer preconditions for the standard Java Development Kit (JDK) library. The results show that our technique can achieve high accuracy with recall from 75–80% and precision from 82–84%. We also found 5 preconditions missing from human written specifications. They were all confirmed by a specification expert. In a user study, participants found 82% of the mined preconditions as a good starting point for writing specifications. Using our mining result, we also built a benchmark of more than 4,000 precondition-related bugs.

An exploratory study of the design impact of language features for aspect-oriented interfaces

A variety of language features to modularize crosscutting concerns have recently been discussed, e.g. open modules, annotation-based pointcuts, explicit join points, and quantified-typed events. All of these ideas are essentially a form of aspect-oriented interface between object-oriented and crosscutting modules, but the representation of this interface differs. While previous works have studied maintenance of AO programs versus OO programs, an empirical comparison of different AO interfaces to each other to investigate their benefits has not been performed. The main contribution of this work is a rigorous empirical study that evaluates the effectiveness of these proposals for AO interfaces towards software maintenance by applying them to 35 different releases of a software product line called MobileMedia and 50 different releases of a web application called Health Watcher. Our comparative analysis using quantitative metrics proposed by Chidamber and Kemerer shows the strengths and weaknesses of these AO interface proposals. Our change impact analysis shows the design stability provided by each of these recent proposals for AO interfaces.

Boa: Ultra-Large-Scale Software Repository and Source-Code Mining

In todays software-centric world, ultra-large-scale software repositories, such as SourceForge, GitHub, and Google Code, are the new library of Alexandria. They contain an enormous corpus of software and related information. Scientists and engineers alike are interested in analyzing this wealth of information. However, systematic extraction and analysis of relevant data from these repositories for testing hypotheses is hard, and best left for mining software repository (MSR) expertse Specifically, mining source code yields significant insights into software development artifacts and processes. Unfortunately, mining source code at a large scale remains a difficult task. Previous approaches had to either limit the scope of the projects studied, limit the scope of the mining task to be more coarse grained, or sacrifice studying the history of the code. In this article we address mining source code: (a) at a very large scale; (b) at a fine-grained level of detail; and (c) with full history information. To address these challenges, we present domain-specific language features for source-code mining in our language and infrastructure called Boa. The goal of Boa is to ease testing MSR-related hypotheses. Our evaluation demonstrates that Boa substantially reduces programming efforts, thus lowering the barrier to entry. We also show drastic improvements in scalability.In todays software-centric world, ultra-large-scale software repositories, such as SourceForge, GitHub, and Google Code, are the new library of Alexandria. They contain an enormous corpus of software and related information. Scientists and engineers alike are interested in analyzing this wealth of information. However, systematic extraction and analysis of relevant data from these repositories for testing hypotheses is hard, and best left for mining software repository (MSR) experts! Specifically, mining source code yields significant insights into software development artifacts and processes. Unfortunately, mining source code at a large scale remains a difficult task. Previous approaches had to either limit the scope of the projects studied, limit the scope of the mining task to be more coarse grained, or sacrifice studying the history of the code. In this article we address mining source code: (a) at a very large scale; (b) at a fine-grained level of detail; and (c) with full history information. To address these challenges, we present domain-specific language features for source-code mining in our language and infrastructure called Boa. The goal of Boa is to ease testing MSR-related hypotheses. Our evaluation demonstrates that Boa substantially reduces programming efforts, thus lowering the barrier to entry. We also show drastic improvements in scalability.

Declarative visitors to ease fine-grained source code mining with full history on billions of AST nodes

Software repositories contain a vast wealth of information about software development. Mining these repositories has proven useful for detecting patterns in software development, testing hypotheses for new software engineering approaches, etc. Specifically, mining source code has yielded significant insights into software development artifacts and processes. Unfortunately, mining source code at a large-scale remains a difficult task. Previous approaches had to either limit the scope of the projects studied, limit the scope of the mining task to be more coarse-grained, or sacrifice studying the history of the code due to both human and computational scalability issues. In this paper we address the substantial challenges of mining source code: a) at a very large scale; b) at a fine-grained level of detail; and c) with full history information. To address these challenges, we present domain-specific language features for source code mining. Our language features are inspired by object-oriented visitors and provide a default depth-first traversal strategy along with two expressions for defining custom traversals. We provide an implementation of these features in the Boa infrastructure for software repository mining and describe a code generation strategy into Java code. To show the usability of our domain-specific language features, we reproduced over 40 source code mining tasks from two large-scale previous studies in just 2 person-weeks. The resulting code for these tasks show between 2.0x--4.8x reduction in code size. Finally we perform a small controlled experiment to gain insights into how easily mining tasks written using our language features can be understood, with no prior training. We show a substantial number of tasks (77%) were understood by study participants, in about 3 minutes per task.

Modular reasoning in the presence of event subtyping

Separating crosscutting concerns while preserving modular reasoning is challenging. Type-based interfaces (event types) separate modularized crosscutting concerns (observers) and traditional object-oriented concerns (subjects). Event types paired with event specifications were shown to be effective in enabling modular reasoning about subjects and observers. Similar to class subtyping, organizing event types into subtyping hierarchies is beneficial. However, unrelated behaviors of observers and their arbitrary execution orders could cause unique, somewhat counterintuitive, reasoning challenges in the presence of event subtyping. These challenges threaten both tractability of reasoning and reuse of event types. This work makes three contributions. First, we pose and explain these challenges. Second, we propose an event-based calculus to show how these challenges can be overcome. Finally, we present modular reasoning rules of our technique and show its applicability to other event-based techniques.

Language features for software evolution and aspect-oriented interfaces: an exploratory study

A variety of language features to modularize cross-cutting concerns have recently been discussed, e.g., open modules, annotation-based pointcuts, explicit join points, and quantified-typed events. All of these ideas are essentially a form of aspect-oriented interface between object-oriented and cross-cutting modules, but the representation of this interface differs. Previous works have studied maintenance benefits of AO programs compared to OO programs, by usually looking at a single AO interface. Other works have looked at several AO interfaces, but only on relatively small systems or systems with only one type of aspectual behavior. Thus, there is a need for a study that examines large, realistic systems for several AO interfaces to determine what problems arise and in which interface(s). The main contribution of this work is a rigorous empirical study that evaluates the effectiveness of these proposals for 4 different AO interfaces by applying them to 35 different releases of a software product line called MobileMedia and 50 different releases of a Web application called Health Watcher. In total, over 400k lines of code were studied across all releases. Our comparative analysis using quantitative metrics proposed by Chidamber and Kemerer shows the strengths and weaknesses of these AO interface proposals. Our change impact analysis shows the design stability provided by each of these recent proposals for AO interfaces.

Supporting dynamic aspect-oriented features

Dynamic aspect-oriented (AO) features have important software engineering benefits such as allowing unanticipated software evolution and maintenance. It is thus important to efficiently support these features in language implementations. Current implementations incur unnecessary design-time and runtime overhead due to the lack of support in underlying intermediate language (IL) models. To address this problem, we present a flexible and dynamic IL model that we call Nu. The Nu model provides a higher level of abstraction compared to traditional object-oriented ILs, making it easier to efficiently support dynamic AO features. We demonstrate these benefits by providing an industrial-strength VM implementation for Nu, by showing translation strategies from dynamic source-level constructs to Nu and by analyzing the performance of the resulting IL code. Nus VM extends the Sun Hotspot VM interpreter and uses a novel caching mechanism to significantly reduce the amortized costs of join point dispatch. Our evaluation using standard benchmarks shows that the overhead of supporting a dynamic deployment model can be reduced to as little as ˜1.5%. Nu provides an improved compilation target for dynamic deployment features, which makes it easier to support such features with corresponding software engineering benefits in software evolution and maintenance and in runtime verification.

Event type polymorphism

Subtype polymorphism is an important feature available in most modern type systems which makes code reuse and specialization possible. Recent works on separation of crosscutting concerns have created event interfaces (types) to decouple subjects from handlers. Extending the notion of subtyping to these event interfaces is a logical step. In this paper, we define event type polymorphism in the context of the Ptolemy language. Ptolemy allows declaring quantified, typed events which provide an interface between subjects and handlers. We add the notion of polymorphic event types to the Ptolemy language, defining a subtype relation among event types which in turn allows for both depth and width subtyping with regard to event context. Since Ptolemy only has explicit event announcement, our semantics is simpler and easier to reason about when compared to previously defined approaches. We also give the first formally defined static semantics for polymorphic events as well as demonstrate its usefulness via examples.

Inferring behavioral specifications from large-scale repositories by leveraging collective intelligence

Despite their proven benefits, useful, comprehensible, and efficiently checkable specifications are not widely available. This is primarily because writing useful, non-trivial specifications from scratch is too hard, time consuming, and requires expertise that is not broadly available. Furthermore, the lack of specifications for widely-used libraries and frameworks, caused by the high cost of writing specifications, tends to have a snowball effect. Core libraries lack specifications, which makes specifying applications that use them expensive. To contain the skyrocketing development and maintenance costs of high assurance systems, this self-perpetuating cycle must be broken. The labor cost of specifying programs can be significantly decreased via advances in specification inference and synthesis, and this has been attempted several times, but with limited success. We believe that practical specification inference and synthesis is an idea whose time has come. Fundamental breakthroughs in this area can be achieved by leveraging the collective intelligence available in software artifacts from millions of open source projects. Fine-grained access to such data sets has been unprecedented, but is now easily available. We identify research directions and report our preliminary results on advances in specification inference that can be had by using such data sets to infer specifications.