3 research outputs found
Stepping Stones to Inductive Synthesis of Low-Level Looping Programs
Inductive program synthesis, from input/output examples, can provide an
opportunity to automatically create programs from scratch without presupposing
the algorithmic form of the solution. For induction of general programs with
loops (as opposed to loop-free programs, or synthesis for domain-specific
languages), the state of the art is at the level of introductory programming
assignments. Most problems that require algorithmic subtlety, such as fast
sorting, have remained out of reach without the benefit of significant
problem-specific background knowledge. A key challenge is to identify cues that
are available to guide search towards correct looping programs. We present
MAKESPEARE, a simple delayed-acceptance hillclimbing method that synthesizes
low-level looping programs from input/output examples. During search, delayed
acceptance bypasses small gains to identify significantly-improved stepping
stone programs that tend to generalize and enable further progress. The method
performs well on a set of established benchmarks, and succeeds on the
previously unsolved "Collatz Numbers" program synthesis problem. Additional
benchmarks include the problem of rapidly sorting integer arrays, in which we
observe the emergence of comb sort (a Shell sort variant that is empirically
fast). MAKESPEARE has also synthesized a record-setting program on one of the
puzzles from the TIS-100 assembly language programming game.Comment: AAAI 201
Code Building Genetic Programming
In recent years the field of genetic programming has made significant
advances towards automatic programming. Research and development of
contemporary program synthesis methods, such as PushGP and Grammar Guided
Genetic Programming, can produce programs that solve problems typically
assigned in introductory academic settings. These problems focus on a narrow,
predetermined set of simple data structures, basic control flow patterns, and
primitive, non-overlapping data types (without, for example, inheritance or
composite types). Few, if any, genetic programming methods for program
synthesis have convincingly demonstrated the capability of synthesizing
programs that use arbitrary data types, data structures, and specifications
that are drawn from existing codebases. In this paper, we introduce Code
Building Genetic Programming (CBGP) as a framework within which this can be
done, by leveraging programming language features such as reflection and
first-class specifications. CBGP produces a computational graph that can be
executed or translated into source code of a host language. To demonstrate the
novel capabilities of CBGP, we present results on new benchmarks that use
non-primitive, polymorphic data types as well as some standard program
synthesis benchmarks.Comment: Proceedings of the 2020 Genetic and Evolutionary Computation
Conference, Genetic Programming Trac
The State of the Art of Automatic Programming
Automaatprogrammeerimine või koodi genereerimine on teatud tüüpi arvutiprogrammide loomisviis, kus kood genereeritakse mõne tööriista abil, mis võimaldab arendajatel koodi kirjutada kõrgemal abstraktsioonitasemel. Selliste programmide rakendamine tarkvaraarenduse protsessis on hea viis programmeerijate produktiivsuse tõstmiseks, võimaldades neil keskenduda pigem käesolevale ülesandele kui implementatsiooni detailidele. Senises teaduskirjanduses on vaadeldud konkreetseid lähenemisi või meetodeid eraldi. Väga vähesed uurimustööd vaatlevad aga kogu valdkonna viimast taset. Käesolevas töös käsitletakse automaatprogrammeerimist olemasoleva kirjanduse süstemaatilise kirjandusülevaate meetodi abil. Töö teeb ülevaate teemaga seonduvatest algoritmidest, probleemidest ning uurmisvaldkonna avatud uurimisküsimustest ning võrdleb valdkonna hetketaset praktika hetketasemega. Vaaldeldud 37 asjakohasest uuringust tegelesid 19 automaatprogrammeerimise üldise määratlemise ja alateemadega. Kolmkümmend uuringut pakkusid välja konkreetse algoritmi või lähenemisviisi. Esitatud tehnikatest rakendati 2 praktikas. Viimasel ajal on automaatprogrammerimise fookus nihkunud programmide sünteesilt induktiivsele programmeerimisele, mille on põhjustanud läbimurded tehisintellekti valdkonnas. Mõistete ja alateemade määratlus on teadlaste vahel ühtne. Õigete spetsifikatsioonide sõnastamine ja piisava teabe andmine automatiseerimiseks on endiselt lahtine uurimisküsimus.Automatic programming or code generation is a type of computer programming where the code is generated using some tools allowing developers to write code at the higher level of abstraction. Implementing these types of programs into the software development process is a good way to boost programmers’ performance by focusing on the task at hand rather than implementation details. Current literature on the subject reviews single approach or method. Very few of them are reviewing state of the art in general. This paper reviews the state of the art of automatic programming by overviewing the existing literature on the topic using systematic literature review method. The paper overviews approaches and algorithms of the topic, examines issues and open questions in the field and compares the state of the art to the state of the practice. Of 37 relevant studies, 19 addressed general definitions and subtopics of automatic programming. 30 presented specific algorithms or approaches. 2 of proposed techniques were implemented in practice. Currently, the focus of automatic programming shifted from program synthesis to inductive programming, caused by a breakthrough in artificial intelligence. Definition of the term and subtopics is consistent between scholars. However, formulating correct specification and providing sufficient information for automation is still an open research question