Raffi Khatchadourian’s Profile Faculty Toggle navigation

Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base-code can lead to join points incorrectly falling in or out of the scope of pointcuts. Deciding which pointcuts have broken because of changes made to the base-code is a daunting task, especially in large and complex systems. This project represents an automated approach that recommends a set of pointcuts that are likely to require modification due to a particular change in the base-code. Our hypothesis is that join points captured by a pointcut exhibit a varying degree of common structural characteristics on multiple levels. We use patterns describing such commonality to recommend pointcuts that have potentially broken to the developer, as well as point the developer in a direction in which the pointcut should be altered. We implemented our approach as an extension to the popular Mylyn Eclipse IDE plugin, which maintains focused contexts of entities relevant to the current task. We show that this approach is useful in revealing broken pointcuts by applying it to multiple versions of several open source projects and evaluating the accuracy of the recommendations produced against actual modifications made on these systems.

Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base-code can lead to join points incorrectly falling in or out of the scope of pointcuts. Deciding which pointcuts have broken because of changes made to the base-code is a daunting task, especially in large and complex systems. This project represents an automated approach that recommends a set of pointcuts that are likely to require modification due to a particular change in the base-code. Our hypothesis is that join points captured by a pointcut exhibit a varying degree of common structural characteristics on multiple levels. We use patterns describing such commonality to recommend pointcuts that have potentially broken to the developer, as well as point the developer in a direction in which the pointcut should be altered. We implemented our approach as an extension to the popular Mylyn Eclipse IDE plugin, which maintains focused contexts of entities relevant to the current task. We show that this approach is useful in revealing broken pointcuts by applying it to multiple versions of several open source projects and evaluating the accuracy of the recommendations produced against actual modifications made on these systems.

Java 8 is one of the largest upgrades to the popular language and framework in over a decade. It offers several new, key features that can help make programs easier to read, write, and maintain, especially in regards to collections. These features include Lambda Expressions, the Stream API, and enhanced interfaces, many of which help bridge the gap between functional and imperative programming paradigms and allow for succinct concurrency implementations. We investigate several open issues related to automatically migrating (refactoring) legacy Java software to use enhanced interfaces correctly, efficiently, and as completely as possible. Our goal is to help developers to maximally understand and adopt this new feature thus improving their software.

Java 8 is one of the largest upgrades to the popular language and framework in over a decade. It offers several new, key features that can help make programs easier to read, write, and maintain, especially in regards to collections. These features include Lambda Expressions, the Stream API, and enhanced interfaces, many of which help bridge the gap between functional and imperative programming paradigms and allow for succinct concurrency implementations. We investigate several open issues related to automatically migrating (refactoring) legacy Java software to use enhanced interfaces correctly, efficiently, and as completely as possible. Our goal is to help developers to maximally understand and adopt this new feature thus improving their software.

Java 8 is one of the largest upgrades to the popular language and framework in over a decade. There are several new key features of Java 8 that can help make programs easier to read, write, and maintain. Java 8 comes with many features, especially related to collection libraries. These include such new features as Lambda Expressions, the Stream API, enhanced interfaces, and more. While JDT Core/UI has incorporated many Java 8 quick fixes and refactorings, there are still many features left to be done. For example, NetBeans has a refactoring that converts loops to Lambda expressions. This project is for exploring the porting of the such conversion mechanism in NetBeans to Eclipse. These may be manifested as refactoring and/or “quick fixes.” This project is open source. Please see the site link for downloads.

Java 8 is one of the largest upgrades to the popular language and framework in over a decade. There are several new key features of Java 8 that can help make programs easier to read, write, and maintain. Java 8 comes with many features, especially related to collection libraries. These include such new features as Lambda Expressions, the Stream API, enhanced interfaces, and more. While JDT Core/UI has incorporated many Java 8 quick fixes and refactorings, there are still many features left to be done. For example, NetBeans has a refactoring that converts loops to Lambda expressions. This project is for exploring the porting of the such conversion mechanism in NetBeans to Eclipse. These may be manifested as refactoring and/or “quick fixes.” This project is open source. Please see the site link for downloads.

The Convert Constants to Enum refactoring for Eclipse provides an automated approach for transforming legacy Java code to use the new enumeration construct. This semantics-preserving tool increases type safety, produces code that is easier to comprehend, removes unnecessary complexity, and eliminates brittleness problems that normally prevent separate compilation.

The Convert Constants to Enum refactoring for Eclipse provides an automated approach for transforming legacy Java code to use the new enumeration construct. This semantics-preserving tool increases type safety, produces code that is easier to comprehend, removes unnecessary complexity, and eliminates brittleness problems that normally prevent separate compilation.

Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base-code can lead to join points incorrectly falling in or out of the scope of pointcuts. In this project, we use an automated approach which limits fragility problems by providing mechanical assistance in pointcut maintenance. The approach is based on harnessing arbitrarily deep structural commonalities between program elements corresponding to join points selected by a pointcut. The extracted patterns are then applied to later versions to offer suggestions of new join points that may require inclusion. To illustrate that the motivation behind our proposal is well-founded, we first empirically establish that join points captured by a single pointcut typically portray a significant amount of unique structural commonality by analyzing patterns extracted from 23 AspectJ programs. Then, we demonstrate the usefulness of our technique by rejuvenating pointcuts in multiple versions of 3 of these programs. The results show that our parameterized heuristic algorithm was able to automatically infer new join points in subsequent versions with an average recall of 0.93. Moreover, these join points appeared, on average, in the top 4th percentile of the suggestions, indicating that the results were precise.

Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base-code can lead to join points incorrectly falling in or out of the scope of pointcuts. In this project, we use an automated approach which limits fragility problems by providing mechanical assistance in pointcut maintenance. The approach is based on harnessing arbitrarily deep structural commonalities between program elements corresponding to join points selected by a pointcut. The extracted patterns are then applied to later versions to offer suggestions of new join points that may require inclusion. To illustrate that the motivation behind our proposal is well-founded, we first empirically establish that join points captured by a single pointcut typically portray a significant amount of unique structural commonality by analyzing patterns extracted from 23 AspectJ programs. Then, we demonstrate the usefulness of our technique by rejuvenating pointcuts in multiple versions of 3 of these programs. The results show that our parameterized heuristic algorithm was able to automatically infer new join points in subsequent versions with an average recall of 0.93. Moreover, these join points appeared, on average, in the top 4th percentile of the suggestions, indicating that the results were precise.