FlashExtract

Resolving conflicts is the main challenge for software merging. The existing merge tools usually rely on the developer to manually resolve conflicts. This is of course inefficient. We propose an interactive approach for resolving merge conflicts. To the best of our knowledge, this is the first attempt for conflict resolution of structured merge. To represent the possibly very large set of candidate programs, we propose an expressive and efficient representation by version space algebra. We also design a simple mechanism for ranking resolutions in the program space, such that the top-ranked resolution is very likely to meet the developer's expectation. We prototype our approach as a merge tool AutoMerge, and evaluate it on 244 real-world conflicts arising from 10 open-source projects. Results show great practicality of our approach. CCS Concepts: • Software and its engineering → Software version control; Context specific languages;

Section: Resolution Rankingmentioning

confidence: 99%

Section: Vsa-based Program Synthesismentioning

confidence: 99%

Conflict resolution for structured merge via version space algebra

Zhu

2018

“…The EUSES corpus collects spreadsheets used as databases, and for financial, grading, homework, inventory, and modeling purposes. EUSES is frequently used by researchers building spreadsheet tools [Alawini et al 2015;Barowy et al 2014Barowy et al , 2015Cheung et al 2016;Grigoreanu et al 2010;Hermans and Dig 2014;Hermans et al 2012aHermans et al , 2010Hermans et al , 2013Hofer et al 2013;Joharizadeh 2015;Le and Gulwani 2014;Muşlu et al 2015;Singh et al 2017]. All of the categories present in EUSES are represented in the CUSTODES suite.…”

Section: About the Benchmarksmentioning

confidence: 99%

ExceLint: automatically finding spreadsheet formula errors

Barowy

Berger

Zorn

2018

Spreadsheets are one of the most widely used programming environments, and are widely deployed in domains like finance where errors can have catastrophic consequences. We present a static analysis specifically designed to find spreadsheet formula errors. Our analysis directly leverages the rectangular character of spreadsheets. It uses an information-theoretic approach to identify formulas that are especially surprising disruptions to nearby rectangular regions. We present ExceLint, an implementation of our static analysis for Microsoft Excel. We demonstrate that ExceLint is fast and effective: across a corpus of 70 spreadsheets, ExceLint takes a median of 5 seconds per spreadsheet, and it significantly outperforms the state of the art analysis.

“…FlashProfile's pattern learner uses the PROSE library (Microsoft 2017d), which implements the FlashMeta framework for inductive program synthesis, specifically programming-by-examples (PBE) Lieberman 2001). PBE has been leveraged by recent works on automating repetitive text-processing tasks, such as string transformation (Gulwani 2011;Singh 2016), extraction (Le and Gulwani 2014), and format normalization (Kini and Gulwani 2015). However, unlike these applications, data profiling does not solicit any (output) examples from the user.…”

Section: Related Workmentioning

confidence: 99%

FlashProfile: a framework for synthesizing data profiles

Padhi

Jain

Perelman

et al. 2018

Self Cite

We address the problem of learning a syntactic profile for a collection of strings, i.e. a set of regex-like patterns that succinctly describe the syntactic variations in the strings. Real-world datasets, typically curated from multiple sources, often contain data in various syntactic formats. Thus, any data processing task is preceded by the critical step of data format identification. However, manual inspection of data to identify the different formats is infeasible in standard big-data scenarios.Prior techniques are restricted to a small set of pre-defined patterns (e.g. digits, letters, words, etc.), and provide no control over granularity of profiles. We define syntactic profiling as a problem of clustering strings based on syntactic similarity, followed by identifying patterns that succinctly describe each cluster. We present a technique for synthesizing such profiles over a given language of patterns, that also allows for interactive refinement by requesting a desired number of clusters.Using a state-of-the-art inductive synthesis framework, PROSE, we have implemented our technique as FlashProfile. Across 153 tasks over 75 large real datasets, we observe a median profiling time of only ∼0.7 s. Furthermore, we show that access to syntactic profiles may allow for more accurate synthesis of programs, i.e. using fewer examples, in programming-by-example (PBE) workflows such as Flash Fill.