Automating string processing in spreadsheets using input-output examples

Section: Introductionmentioning

confidence: 99%

Static Analysis of String Values

Costantini

Ferrara

Cortesi

2011

“…Moreover, in many practical applications the ranking functions are very simple. For example, the popular spreadsheet formula synthesizer FlashFill [12] uses a ranking function to prefer small programs with few constants. This type of objective is expressible in our framework.…”

Section: Related Workmentioning

confidence: 99%

Syntax-Guided Synthesis with Quantitative Syntactic Objectives

D’Antoni

2018

Abstract. Automatic program synthesis promises to increase the productivity of programmers and end-users of computing devices by automating tedious and error-prone tasks. Despite the practical successes of program synthesis, we still do not have systematic frameworks to synthesize programs that are "good" according to certain metricse.g., produce programs of reasonable sizes or with good runtime-and to understand when synthesis can result in such good programs. In this paper, we propose QSyGuS, a unifying framework for describing syntaxguided synthesis problems with quantitative objectives over the syntax of the synthesized programs. QSyGuS builds on weighted (tree) grammars, a clean and foundational formalism that provides flexible support for different quantitative objectives, useful closure properties, and practical decision procedures. We then present an algorithm for solving QSyGuS. Our algorithm leverages closure properties of weighted grammars to generate intermediate problems that can be solved using non-quantitative SyGuS solvers. Finally, we implement our algorithm in a tool, QuaSi, and evaluate it on 26 quantitative extensions of existing SyGuS benchmarks. QuaSi can synthesize optimal solutions in 15/26 benchmarks with times comparable to those needed to find an arbitrary solution.

“…Their technique works by identifying previous incorrect subprograms and replacing them with correct subprograms. They demonstrated their technique on a set of string transformation problems and compared the results with the Flashfill approach [7] and Metagol DF [9]. While the overall aims of their approach are similar to ours, their approach does not support automatic problem decomposition using predicate invention, nor learning recursive programs.…”

Section: Related Workmentioning

confidence: 99%

Meta-Interpretive Learning of Data Transformation Programs

Cropper

Tamaddoni‐Nezhad

Muggleton

2016

Abstract. Data transformation involves the manual construction of large numbers of special-purpose programs. Although typically small, such programs can be complex, involving problem decomposition, recursion, and recognition of context. Building such programs is common in commercial and academic data analytic projects and can be labour intensive and expensive, making it a suitable candidate for machine learning. In this paper, we use the meta-interpretive learning framework (MIL) to learn recursive data transformation programs from small numbers of examples. MIL is well suited to this task because it supports problem decomposition through predicate invention, learning recursive programs, learning from few examples, and learning from only positive examples. We apply Metagol, a MIL implementation, to both semi-structured and unstructured data. We conduct experiments on three real-world datasets: medical patient records, XML mondial records, and natural language taken from ecological papers. The experimental results suggest that high levels of predictive accuracy can be achieved in these tasks from small numbers of training examples, especially when learning with recursion.