We present KumQuat, a system for automatically generating data-parallel implementations of Unix shell commands and pipelines. The generated parallel versions split input streams, execute multiple instantiations of the original pipeline commands to process the splits in parallel, then combine the resulting parallel outputs to produce the final output stream. KumQuat automatically synthesizes the combine operators, with a domain-specific combiner language acting as a strong regularizer that promotes efficient inference of correct combiners. We present experimental results that show that these combiners enable the effective parallelization of our benchmark scripts.Paper (pdf)
We present Konure, a new system that uses active learning to infer models of applications that retrieve data from relational databases. Konure comprises a domain-specific language (each model is a program in this language) and associated inference algorithm that infers models of applications whose behavior can be expressed in this language. The inference algorithm generates inputs and database configurations, runs the application, then observes the resulting database traffic and outputs to progressively refine its current model hypothesis. Because the technique works with only externally observable inputs, outputs, and database configurations, it can infer the behavior of applications written in arbitrary languages using arbitrary coding styles (as long as the behavior of the application is expressible in the domain-specific language). Konure also implements a regenerator that produces a translated Python implementation of the application that systematically includes relevant security and error checks.Paper (pdf)
Background: Application frameworks, such as Ruby on Rails, introduce abstractions with the goal of simplifying development for particular application domains, such as web development. While experts enjoy increased productivity due to these abstractions, the flow of the programs is often hard to understand for non-experts and newcomers due to implicit flow and concealed lower level action that seems like "magic".
Objective: We conjecture that converting these implicit flows into an explicit and unified form can help non-experts comprehend the programs using these frameworks. We call the process of unifying distributed, implicit flows into a single routine deimplicitization.
Method: We want to conduct an experiment that studies the impact of deimplicitization on program comprehension. Particularly, we want to study how software developers with different expertise (novices/students, framework experts/professional developers) can answer comprehension questions differently with respect to time and correctness, under the treatments of either a deimplicitized version of the program in Python or the original version of the program in Ruby on Rails.
Software applications have grown increasingly complex to deliver the features desired by users. Software modularity has been used as a way to mitigate the costs of developing such complex software. Active learning-based program inference provides an elegant framework that exploits this modularity to tackle development correctness, performance and cost in large applications. Inferred programs can be used for many purposes, including generation of secure code, code re-use through automatic encapsulation, adaptation to new platforms or languages, and optimization. We show through detailed examples how our approach can infer three modules in a representative application. Finally, we outline the broader paradigm and open research questions.Paper (pdf)
We present a study that characterizes the way developers use automatically generated patches when fixing software defects. Our study tasked two groups of developers with repairing defects in C programs. Both groups were provided with the defective line of code. One was also provided with five automatically generated and validated patches, all of which modified the defective line of code, and one of which was correct. Contrary to our initial expectations, the group with access to the generated patches did not produce more correct patches and did not produce patches in less time. We characterize the main behaviors observed in experimental subjects: a focus on understanding the defect and the relationship of the patches to the original source code. Based on this characterization, we highlight various potentially productive directions for future developer-centric automatic patch generation systems.Paper (pdf)
We present Konure, a new system that uses active learning to infer models of applications that access relational databases. Konure comprises a domain-specific language (each model is a program in this language) and associated inference algorithm that infers models of applications whose behavior can be expressed in this language. The inference algorithm generates inputs and database configurations, runs the application, then observes the resulting database traffic and outputs to progressively refine its current model hypothesis. Because the technique works with only externally observable inputs, outputs, and database configurations, it can infer the behavior of applications written in arbitrary languages using arbitrary coding styles (as long as the behavior of the application is expressible in the domain-specific language). Konure also implements a regenerator that produces a translated Python implementation of the application that systematically includes relevant security and error checks.Paper (pdf)
As modern computation platforms become increasingly complex, their programming interfaces are increasingly difficult to use. This complexity is especially inappropriate given the relatively simple core functionality that many of the computations implement. We present a new approach for obtaining software that executes on modern computing platforms with complex programming interfaces. Our approach starts with a simple seed program, written in the language of the developer's choice, that implements the desired core functionality. It then systematically generates inputs and observes the resulting outputs to learn the core functionality. It finally automatically regenerates new code that implements the learned core functionality on the target computing platform. This regenerated code contains boilerplate code for the complex programming interfaces that the target computing platform presents. By providing a productive new mechanism for capturing and encapsulating knowledge about how to use modern complex interfaces, this new approach promises to greatly reduce the developer effort required to obtain secure, robust software that executes on modern computing platforms.Paper (pdf)
We present a new language construct, filtered iterators, for robust input processing. Filtered iterators are designed to eliminate many common input processing errors while enabling robust continued execution. The design is inspired by (1) observed common input processing errors and (2) successful strategies implemented by human developers fixing input processing errors. Filtered iterators decompose inputs into input units and atomically and automatically discard units that trigger errors. Statistically significant results from a developer study demonstrate the effectiveness of filtered iterators in enabling developers to produce robust input processing code without common input processing defects.Paper (pdf)
(1: Equal contribution.)