An analytical inductive functional programming system that avoids unintended programs

Katayama, Susumu

doi:10.1145/2103746.2103758

Cited by 23 publications

(12 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For brevity, we restrict ourselves to the (considerable) literature that focuses on using types as specifications, and omit discussing methods that use e.g. input-output examples or tests [Gulwani 2011;Katayama 2012;Lee et al 2018;Osera and Zdancewic 2015], logical specifications [Galenson et al 2014;Srivastava et al 2010] or program sketches [Solar-Lezama 2008].…”

Section: Related Workmentioning

confidence: 99%

Program synthesis by type-guided abstraction refinement

Guo

James

Justo

et al. 2019

Proc. ACM Program. Lang.

View full text Add to dashboard Cite

We consider the problem of type-directed component-based synthesis where, given a set of (typed) components and a query type, the goal is to synthesize a term that inhabits the query. Classical approaches based on proof search in intuitionistic logics do not scale up to the standard libraries of modern languages, which span hundreds or thousands of components. Recent graph reachability based methods proposed for Java do scale, but only apply to monomorphic data and components: polymorphic data and components infinitely explode the size of the graph that must be searched, rendering synthesis intractable. We introduce type-guided abstraction refinement (TYGAR), a new approach for scalable type-directed synthesis over polymorphic datatypes and components. Our key insight is that we can overcome the explosion by building a graph over abstract types which represent a potentially unbounded set of concrete types. We show how to use graph reachability to search for candidate terms over abstract types, and introduce a new algorithm that uses proofs of untypeability of ill-typed candidates to iteratively refine the abstraction until a well-typed result is found. We have implemented TYGAR in H+, a tool that takes as input a set of Haskell libraries and a query type, and returns a Haskell term that uses functions from the provided libraries to implement the query type. Our support for polymorphism allows H+ to work with higher-order functions and type classes, and enables more precise queries due to parametricity. We have evaluated H+ on 44 queries using a set of popular Haskell libraries with a total of 291 components. H+ returns an interesting solution within the first five results for 32 out of 44 queries. Our results show that TYGAR allows H+ to rapidly return well-typed terms, with the median time to first solution of just 1.4 seconds. Moreover, we observe that gains from iterative refinement over exhaustive enumeration are more pronounced on harder queries. CCS Concepts: • Theory of computation → Automated reasoning; • Software and its engineering → Automatic programming.

show abstract

Section: Related Workmentioning

confidence: 99%

Program synthesis by type-guided abstraction refinement

Guo

James

Justo

et al. 2019

Proc. ACM Program. Lang.

View full text Add to dashboard Cite

show abstract

“…Djinn derives Haskell programs from types [4] (but not examples) according to a modification of Gentzen's LJ [8]. In contrast, Katayama's MagicHaskeller [13] enumerates programs using a set of logical rules and permitted components, caching them for later lookup. It is fast, but does not generate programs that branch.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

Type-and-example-directed program synthesis

OseraPeter-Michael

ZdancewicSteve

2015

SIGPLAN Not.

View full text Add to dashboard Cite

This paper presents an algorithm for synthesizing recursive functions that process algebraic datatypes. It is founded on proof-theoretic techniques that exploit both type information and input-output examples to prune the search space. The algorithm uses refinement trees, a data structure that succinctly represents constraints on the shape of generated code. We evaluate the algorithm by using a prototype implementation to synthesize more than 40 benchmarks and several non-trivial larger examples. Our results demonstrate that the approach meets or outperforms the state-of-the-art for this domain, in terms of synthesis time or attainable size of the generated programs.

show abstract

“…One alternative to DSLs is general-purpose declarative (programming) languages (GPDL). These languages are the base for most of the inductive programming systems created during the last two decades, such as Progol [128], MagicHaskeller [90], FLIP [45,72], Metagol [131], gErl [116] and many others. Unfortunately, inductive programming using GPDLs is usually inefficient and/or incomplete because of the large search space, as they are not specialised for a particular domain.…”

Section: Idmentioning

confidence: 99%

“…Table 4.3 shows some of these functions 1 . Although MagicHaskeller is able to solve many string and Boolean problems by using its default library [90], this list of functions is not enough to solve more complex problems. For instance, the example shown previously in Table 4.1 is impossible to solve with MagicHaskeller's default library since there is a need to replace each dash symbol ('-') with a slash symbol ('/'), and MagicHaskeller is unable to generate or use any character or digit if it is not defined as constant in its library or if it is not provided as an input parameter.…”

Section: Domain-specific Inductionmentioning

confidence: 99%

“…This description of the system has been published in [22,23]. BK-ADAPT uses MagicHaskeller [90] as the underlying IP system. BK-ADAPT works as follows: (1) one output example is filled (See Figure D.1) and used to detect the appropriate domain (See Figure D.2) and set of primitives that form the dynamic background knowledge ; (2) using the background knowledge and the example, MagicHaskeller learns a function f that correctly transforms the input of the example to the given output (See Figure D.3); and (3) the function f is applied to the rest of the inputs, obtaining the new values for the output column automatically (See Figure D.4).…”

Section: Algorithm 1 Automat[r]ix : Selecting Matrix Operations By Exmentioning

confidence: 99%

See 1 more Smart Citation

Towards Data Wrangling Automation through Dynamically-Selected Background Knowledge

Ochando¹

View full text Add to dashboard Cite

Data science is essential for the extraction of value from data. However, the most tedious part of the process, data wrangling, implies a range of mostly manual formatting, identification and cleansing manipulations. Data wrangling still resists automation partly because the problem strongly depends on domain information, which becomes a bottleneck for state-of-the-art systems as the diversity of domains, formats and structures of the data increases. In this thesis we focus on generating algorithms that take advantage of the domain knowledge for the automation of parts of the data wrangling process. We illustrate the way in which general program induction techniques, instead of domain-specific languages, can be applied flexibly to problems where knowledge is important, through the dynamic use of domain-specific knowledge. More generally, we argue that a combination of knowledge-based and dynamic learning approaches leads to successful solutions. We propose several strategies to automatically select or construct the appropriate background knowledge for several data wrangling scenarios. The key idea is based on choosing the best specialised background primitives according to the context of the particular problem to solve. We address two scenarios. In the first one, we handle personal data (names, dates, telephone numbers, etc.) that are presented in very different string formats and have to be transformed into a unified format. The problem is how to build a compositional transformation from a large set of primitives in the domain (e.g., handling months, years, days of the week, etc.). We develop a system (BK-ADAPT) that guides the search through the background knowledge by extracting several meta-features from the examples characterising the column domain. In the second scenario, we face the transformation of data matrices in generic programming languages such as R, using an input matrix and some cells of the output matrix as examples. We also develop a system guided by a treebased search (AUTOMAT[R]IX) that uses several constraints, prior primitive probabilities and textual hints to efficiently learn the transformations. With these systems, we show that the combination of inductive programming with the dynamic selection of the appropriate primitives from the background knowledge is able to improve the results of other state-of-the-art-and more specific-data wrangling approaches.

show abstract

An analytical inductive functional programming system that avoids unintended programs

Cited by 23 publications

References 13 publications

Program synthesis by type-guided abstraction refinement

Program synthesis by type-guided abstraction refinement

Type-and-example-directed program synthesis

Towards Data Wrangling Automation through Dynamically-Selected Background Knowledge

Contact Info

Product

Resources

About