Crossing the gap from imperative to functional programming through refactoring

ACM Trans. Softw. Eng. Methodol.

2015

Self Cite

Despite significant progress in recent years, the important problem of static race detection remains open. Previous techniques took a general approach and looked for races by analyzing the effects induced by lowlevel concurrency constructs (e.g., java.lang.Thread). But constructs and libraries for expressing parallelism at a higher level (e.g., fork-join, futures, parallel loops) are becoming available in all major programming languages. We claim that specializing an analysis to take advantage of the extra semantic information provided by the use of these constructs and libraries improves precision and scalability.We present ITERACE, a set of techniques that are specialized to use the intrinsic thread, safety, and dataflow structure of collections and of the new loop parallelism mechanism introduced in Java 8. Our evaluation shows that ITERACE is fast and precise enough to be practical. It scales to programs of hundreds of thousands of lines of code and reports very few race warnings, thus avoiding a common pitfall of static analyses. In five out of the seven case studies, ITERACE reported no false warnings. Also, it revealed six bugs in real-world applications. We reported four of them: one had already been fixed, and three were new and the developers confirmed and fixed them.Furthermore, we evaluate the effect of each specialization technique on the running time and precision of the analysis. For each application, we run the analysis under 32 different configurations. This allows to analyze each technique's effect both alone and in all possible combinations with other techniques. ACM Reference Format:Cosmin Radoi and Danny Dig. 2015. Effective techniques for static race detection in Java parallel loops.

Section: Resultsmentioning

confidence: 99%

Effective Techniques for Static Race Detection in Java Parallel Loops

Radoi

ACM Trans. Softw. Eng. Methodol.

2015

Self Cite

“…Thus, IteRace can also be used in scenarios with high interactivity, e.g., refactoring for parallelism [23,24,32], that require fast and precise analyses.…”

Section: Resultsmentioning

confidence: 99%

Practical static race detection for Java parallel loops

Radoi

Proceedings of the 2013 International Symposium on Software Testing and Analysis

2013

Self Cite

Despite significant progress in recent years, the important problem of static race detection remains open. Previous techniques took a general approach and looked for races by analyzing the effects induced by low-level concurrency constructs (e.g., java.lang.Thread). But constructs and libraries for expressing parallelism at a higher level (e.g., fork-join, futures, parallel loops) are becoming available in all major programming languages. We claim that specializing an analysis to take advantage of the extra semantic information provided by the use of these constructs and libraries improves precision and scalability.We present IteRace, a set of techniques that are specialized to use the intrinsic thread, safety, and data-flow structure of collections and of the new loop-parallelism mechanism to be introduced in Java 8. Our evaluation shows that IteRace is fast and precise enough to be practical. It scales to programs of hundreds of thousands of lines of code and it reports few race warnings, thus avoiding a common pitfall of static analyses. The tool revealed six bugs in real-world applications. We reported four of them, one had already been fixed, and three were new and the developers confirmed and fixed them.

“…However, given the cutting edge nature of the Cloud Data Types, we could not find such developers. Thus, we chose to use indirect metrics, as it is [15,33]. Second, are we sure that the performed refactorings are accurate?…”

Section: Threats To Validitymentioning

confidence: 99%

“…While the original research was using refactoring to improve the design of existing programs, the more recent work used refactoring techniques to retrofit concurrency [34,11], functional features [15], etc. As far as we know, we present the first automated tool to introduce mobile cloud computing via an automated refactoring tool.…”

Section: Related Workmentioning

confidence: 99%

Refactoring local to cloud data types for mobile apps

Hilton

Christi

Proceedings of the 1st International Conference on Mobile Software Engineering and Systems

et al. 2014

Self Cite

Mobile cloud computing can greatly enrich the capabilities of today's pervasive mobile devices. Storing data on the cloud can enable features such as automatic backup, seamless transition between multiple devices, and multiuser support for existing apps. However, the process of converting local into cloud data types requires high expertise, is difficult, and time-consuming. Refactoring techniques can greatly simplify this process.In this paper we present a formative study where we analyzed and successfully converted four real-world touchdevelop apps into cloud-enabled apps. Based on these lessons, we designed and implemented, Cloudifyer, a tool that automatically refactors local data types into cloud data types on the touchdevelop platform. Our empirical evaluation on a corpus of 123 mobile apps resulting in 2722 transformations shows (i) that the refactoring is widely applicable, (ii) Cloudifyer saves human effort, and (iii) Cloudifyer is accurate.