Generalized data structure synthesis

Data structure synthesis is the task of generating data structure implementations from high-level specifications. Recent work in this area has shown potential to save programmer time and reduce the risk of defects. Existing techniques focus on data structures for manipulating subsets of a single col...

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Vydáno v:2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) s. 958 - 968
Hlavní autoři: Loncaric, Calvin, Ernst, Michael D., Torlak, Emina
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 27.05.2018
Edice:ACM Conferences
Témata:
automatic programming
Data structures
Program synthesis
Servers
Software
Software and its engineering > Software notations and tools > Compilers > Source code generation
Synthesizers
Task analysis
Theory of computation > Design and analysis of algorithms > Data structures design and analysis
program synthesis
automatic programming
data structures
ISBN:9781450356381, 1450356389
ISSN:1558-1225
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Abstract Data structure synthesis is the task of generating data structure implementations from high-level specifications. Recent work in this area has shown potential to save programmer time and reduce the risk of defects. Existing techniques focus on data structures for manipulating subsets of a single collection, but real-world programs often track multiple related collections and aggregate properties such as sums, counts, minimums, and maximums. This paper shows how to synthesize data structures that track subsets and aggregations of multiple related collections. Our technique decomposes the synthesis task into alternating steps of query synthesis and incrementalization. The query synthesis step implements pure operations over the data structure state by leveraging existing enumerative synthesis techniques, specialized to the data structures domain. The incrementalization step implements imperative state modifications by re-framing them as fresh queries that determine what to change, coupled with a small amount of code to apply the change. As an added benefit of this approach over previous work, the synthesized data structure is optimized for not only the queries in the specification but also the required update operations. We have evaluated our approach in four large case studies, demonstrating that these extensions are broadly applicable.
AbstractList Data structure synthesis is the task of generating data structure implementations from high-level specifications. Recent work in this area has shown potential to save programmer time and reduce the risk of defects. Existing techniques focus on data structures for manipulating subsets of a single collection, but real-world programs often track multiple related collections and aggregate properties such as sums, counts, minimums, and maximums. This paper shows how to synthesize data structures that track subsets and aggregations of multiple related collections. Our technique decomposes the synthesis task into alternating steps of query synthesis and incrementalization. The query synthesis step implements pure operations over the data structure state by leveraging existing enumerative synthesis techniques, specialized to the data structures domain. The incrementalization step implements imperative state modifications by re-framing them as fresh queries that determine what to change, coupled with a small amount of code to apply the change. As an added benefit of this approach over previous work, the synthesized data structure is optimized for not only the queries in the specification but also the required update operations. We have evaluated our approach in four large case studies, demonstrating that these extensions are broadly applicable.
Data structure synthesis is the task of generating data structure implementations from high-level specifications. Recent work in this area has shown potential to save programmer time and reduce the risk of defects. Existing techniques focus on data structures for manipulating subsets of a single collection, but real-world programs often track multiple related collections and aggregate properties such as sums, counts, minimums, and maximums. This paper shows how to synthesize data structures that track subsets and aggregations of multiple related collections. Our technique decomposes the synthesis task into alternating steps of query synthesis and incrementalization. The query synthesis step implements pure operations over the data structure state by leveraging existing enumerative synthesis techniques, specialized to the data structures domain. The incrementalization step implements imperative state modifications by re-framing them as fresh queries that determine what to change, coupled with a small amount of code to apply the change. As an added benefit of this approach over previous work, the synthesized data structure is optimized for not only the queries in the specification but also the required update operations. We have evaluated our approach in four large case studies, demonstrating that these extensions are broadly applicable.
Author Loncaric, Calvin
Torlak, Emina
Ernst, Michael D.
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  surname: Torlak
  fullname: Torlak, Emina
  email: emina@cs.washington.edu
  organization: University of Washington
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Keywords program synthesis
automatic programming
data structures
Language English
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Snippet Data structure synthesis is the task of generating data structure implementations from high-level specifications. Recent work in this area has shown potential...
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StartPage 958
SubjectTerms automatic programming
Data structures
Program synthesis
Servers
Software
Software and its engineering -- Software notations and tools -- Compilers -- Source code generation
Synthesizers
Task analysis
Theory of computation -- Design and analysis of algorithms -- Data structures design and analysis
Title Generalized data structure synthesis
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