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Towards Safe Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Takvor Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Towards Safe Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Takvor Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Publications and Research
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution. We present our ongoing work on automated refactoring that assists developers in specifying whether …
Towards Safe Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi T. Khatchadourian Ph,D,, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Towards Safe Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi T. Khatchadourian Ph,D,, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Publications and Research
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution. We present our ongoing work on automated refactoring that assists developers in specifying whether …
An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja
An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja
Publications and Research
Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today’s data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source …
An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja
An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja
Publications and Research
Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today's data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source …
Safe Automated Refactoring For Intelligent Parallelization Of Java 8 Streams, Raffi T. Khatchadourian, Yiming Tang, Mehdi Bagherzadeh, Syed Ahmed
Safe Automated Refactoring For Intelligent Parallelization Of Java 8 Streams, Raffi T. Khatchadourian, Yiming Tang, Mehdi Bagherzadeh, Syed Ahmed
Publications and Research
Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages and platforms. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite, e.g., collections, and infinite data structures. However, using this API efficiently involves subtle considerations such as determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. Also, streams may not run all operations in parallel depending on particular collectors used in reductions. In …
Safe Automated Refactoring For Intelligent Parallelization Of Java 8 Streams, Raffi T. Khatchadourian, Yiming Tang, Mehdi Bagherzadeh, Syed Ahmed
Safe Automated Refactoring For Intelligent Parallelization Of Java 8 Streams, Raffi T. Khatchadourian, Yiming Tang, Mehdi Bagherzadeh, Syed Ahmed
Publications and Research
Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The …
A Tool For Optimizing Java 8 Stream Software Via Automated Refactoring, Raffi T. Khatchadourian, Yiming Tang, Mehdi Bagherzadeh, Syed Ahmed
A Tool For Optimizing Java 8 Stream Software Via Automated Refactoring, Raffi T. Khatchadourian, Yiming Tang, Mehdi Bagherzadeh, Syed Ahmed
Publications and Research
Streaming APIs are pervasive in mainstream Object-Oriented languages. For example, the Java 8 Stream API allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we describe the engineering aspects of an open source automated refactoring tool called Optimize Streams that assists developers in writing optimal stream software in …
Poster: Towards Safe Refactoring For Intelligent Parallelization Of Java 8 Streams, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Syed Ahmed
Poster: Towards Safe Refactoring For Intelligent Parallelization Of Java 8 Streams, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Syed Ahmed
Publications and Research
The Java 8 Stream API sets forth a promising new programming model that incorporates functional-like, MapReduce-style features into a mainstream programming language. However, using streams correctly and efficiently may involve subtle considerations. In this poster, we present our ongoing work and preliminary results towards an automated refactoring approach that assists developers in writing optimal stream code. The approach, based on ordering and typestate analysis, determines when it is safe and advantageous to convert streams to parallel and optimize a parallel streams.
Poster: Towards Safe Refactoring For Intelligent Parallelization Of Java 8 Streams, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Syed Ahmed
Poster: Towards Safe Refactoring For Intelligent Parallelization Of Java 8 Streams, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Syed Ahmed
Publications and Research
The Java 8 Stream API sets forth a promising new programming model that incorporates functional-like, MapReduce-style features into a mainstream programming language. However, using streams correctly and efficiently may involve subtle considerations. In this poster, we present our ongoing work and preliminary results to- wards an automated refactoring approach that assists developers in writing optimal stream code. The approach, based on ordering and typestate analysis, determines when it is safe and advantageous to convert streams to parallel and optimize a parallel streams.
Defaultification Refactoring: A Tool For Automatically Converting Java Methods To Default, Raffi T. Khatchadourian, Hidehiko Masuhara
Defaultification Refactoring: A Tool For Automatically Converting Java Methods To Default, Raffi T. Khatchadourian, Hidehiko Masuhara
Publications and Research
Enabling interfaces to declare (instance) method implementations, Java 8 default methods can be used as a substitute for the ubiquitous skeletal implementation software design pattern. Performing this transformation on legacy software manually, though, may be non-trivial. The refactoring requires analyzing complex type hierarchies, resolving multiple implementation inheritance issues, reconciling differences between class and interface methods, and analyzing tie-breakers (dispatch precedence) with overriding class methods. All of this is necessary to preserve type-correctness and confirm semantics preservation. We demonstrate an automated refactoring tool called Migrate Skeletal Implementation to Interface for transforming legacy Java code to use the new default construct. The …
Automated Refactoring Of Legacy Java Software To Default Methods, Raffi T. Khatchadourian, Hidehiko Masuhara
Automated Refactoring Of Legacy Java Software To Default Methods, Raffi T. Khatchadourian, Hidehiko Masuhara
Publications and Research
Java 8 default methods, which allow interfaces to contain (instance) method implementations, are useful for the skeletal implementation software design pattern. However, it is not easy to transform existing software to exploit default methods as it requires analyzing complex type hierarchies, resolving multiple implementation inheritance issues, reconciling differences between class and interface methods, and analyzing tie-breakers (dispatch precedence) with overriding class methods to preserve type-correctness and confirm semantics preservation. In this paper, we present an efficient, fully-automated, type constraint-based refactoring approach that assists developers in taking advantage of enhanced interfaces for their legacy Java software. The approach features an extensive …
Automated Refactoring Of Legacy Java Software To Default Methods, Raffi T. Khatchadourian, Hidehiko Masuhara
Automated Refactoring Of Legacy Java Software To Default Methods, Raffi T. Khatchadourian, Hidehiko Masuhara
Publications and Research
Java 8 default methods, which allow interfaces to contain (instance) method implementations, are useful for the skeletal implementation software design pattern. However, it is not easy to transform existing software to exploit default methods as it requires analyzing complex type hierarchies, resolving multiple implementation inheritance issues, reconciling differences between class and interface methods, and analyzing tie-breakers (dispatch precedence) with overriding class methods to preserve type-correctness and confirm semantics preservation. In this paper, we present an efficient, fully-automated, type constraint-based refactoring approach that assists developers in taking advantage of enhanced interfaces for their legacy Java software. The approach features an extensive …
Towards Improving Interface Modularity In Legacy Java Software Through Automated Refactoring, Raffi T. Khatchadourian, Olivia Moore, Hidehiko Masuhara
Towards Improving Interface Modularity In Legacy Java Software Through Automated Refactoring, Raffi T. Khatchadourian, Olivia Moore, Hidehiko Masuhara
Publications and Research
The skeletal implementation pattern is a software design pattern that defines an abstract class that provides a partial interface implementation. However, since Java allows only single class inheritance, if implementers decide to extend a skeletal implementation, they will not be allowed to extend any other class. Also, discovering the skeletal implementation may require a global analysis. Java 8 enhanced interfaces alleviate these problems by enabling interfaces to contain (default) method implementations, which implementers inherit. Java classes are then free to extend a different class, and a separate abstract class is no longer needed; developers considering implementing an interface need only …
Towards Improving Interface Modularity In Legacy Java Software Through Automated Refactoring, Raffi T. Khatchadourian, Olivia Moore, Hidehiko Masuhara
Towards Improving Interface Modularity In Legacy Java Software Through Automated Refactoring, Raffi T. Khatchadourian, Olivia Moore, Hidehiko Masuhara
Publications and Research
The skeletal implementation pattern is a software design pattern consisting of defining an abstract class that provides a partial interface implementation. However, since Java allows only single class inheritance, if implementers decide to extend a skeletal implementation, they will not be allowed to extend any other class. Also, discovering the skeletal implementation may require a global analysis. Java 8 enhanced interfaces alleviate these problems by allowing interfaces to contain (default) method implementations, which implementers inherit. Java classes are then free to extend a different class, and a separate abstract class is no longer needed; developers considering implementing an interface need …
Porting The Netbeans Java 8 Enhanced For Loop Lambda Expression Refactoring To Eclipse, Md Arefin, Raffi T. Khatchadourian
Porting The Netbeans Java 8 Enhanced For Loop Lambda Expression Refactoring To Eclipse, Md Arefin, Raffi T. Khatchadourian
Publications and Research
Java 8 is one of the largest upgrades to the popular language and framework in over a decade. However, the Eclipse IDE is missing several key refactorings that could help developers take advantage of new features in Java 8 more easily. In this paper, we discuss our ongoing work in porting the enhanced for loop to lambda expression refactoring from the NetBeans IDE to Eclipse. We also discuss future plans for new Java 8 refactorings not found in any current IDE.
Porting The Netbeans Java 8 Enhanced For Loop Lambda Expression Refactoring To Eclipse, Md Arefin, Raffi T. Khatchadourian
Porting The Netbeans Java 8 Enhanced For Loop Lambda Expression Refactoring To Eclipse, Md Arefin, Raffi T. Khatchadourian
Publications and Research
Java 8 is one of the largest upgrades to the popular language and framework in over a decade. However, the Eclipse IDE is missing several key refactorings that could help developers take advantage of new features in Java 8 more easily. In this paper, we discuss our ongoing work in porting the enhanced for loop to lambda expression refactoring from the NetBeans IDE to Eclipse. We also discuss future plans for new Java 8 refactorings not found in any current IDE.
Rejuvenate Pointcut: A Tool For Pointcut Expression Recovery In Evolving Aspect-Oriented Software, Raffi T. Khatchadourian, Awais Rashid
Rejuvenate Pointcut: A Tool For Pointcut Expression Recovery In Evolving Aspect-Oriented Software, Raffi T. Khatchadourian, Awais Rashid
Publications and Research
Aspect-oriented programming (AOP) strives to localize the scattered and tangled implementations of crosscutting concerns (CCCs) by allowing developers to declare that certain actions (advice) should be taken at specific points (join points) during the execution of software where a CCC (an aspect) is applicable. However, it is non-trivial to construct optimal pointcut expressions (a collection of join points) that capture the true intentions of the programmer and, upon evolution, maintain these intentions. We demonstrate an AspectJ source-level inferencing tool called rejuvenate pointcut which helps developers maintain pointcut expressions over the lifetime of a software product. A key insight into the …