A Reference Interpreter for the Graph Programming Language GP 2

Bak, Christopher; Faulkner, Glyn; Plump, Detlef; Runciman, Colin

doi:10.4204/eptcs.181.4

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…The design of language GP2 [12] is close to small-t ALC . Building blocks in GP programs are conditional rule schemata whose nodes and edges are labeled by sequences of expressions over parameters of type integer, string and list.…”

Section: Discussionmentioning

confidence: 96%

Combining Dynamic and Static Analysis to Help Develop Correct Graph Transformations

et al. 2016

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Developing provably correct graph transformations is not a trivial task. Besides writing the code, a developer must as well specify the pre and post conditions. The objective of our work is to assist developers in producing such a Hoare triple in order to submit it to a formal verification tool. By combining static and dynamic analysis, we aim at providing more useful feedback to developers. Dynamic analysis helps identify inconsistencies between the code and its specifications. Static analysis facilitates extracting the pre and post conditions from the code. Based on this proposal, we implemented a prototype that allows running, testing and proving graph transformations written in small t ALC , our own transformation language.

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Section: Discussionmentioning

confidence: 96%

Combining Dynamic and Static Analysis to Help Develop Correct Graph Transformations

et al. 2016

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“…Dari kedua dataset yang digunakan, dilakukan beberapa tahap preprocessing dengan menggunakan Python. Judul film yang mengandung huruf non-ASCII ditransliterasi ke ASCII, karena string yang diterima program GP 2 adalah yang mengandung karakter ASCII [15]. Karakter yang tidak kompatibel dengan GP 2 atau GP 2 Online Graph Visualiser juga diganti atau dihapus.…”

Section: Data Preprocessingunclassified

Pembangunan Knowledge Graph dan Sistem Rekomendasi Film dengan GP 2

Zanzabila,

Wulandari,

Ummah

2023

logic

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Film, sebagai salah satu jenis hiburan yang banyak diminati, saat ini sudah berlimpah banyaknya. Setiap orang memiliki pendapat yang berbeda terhadap film-film tersebut. Sistem rekomendasi dapat dimanfaatkan untuk memberikan rekomendasi film yang terpersonalisasi untuk tiap orang, sehingga setiap orang dapat mudah memilih film yang akan mereka tonton. Saat ini sudah banyak sistem rekomendasi film yang digunakan. Metode yang digunakan pun bervariasi, seperti dengan sentiment analysis, collaborative filtering, atau dengan pembangunan knowledge graph. Namun, sistem rekomendasi film dengan knowledge graph yang sudah dilakukan sampai saat ini belum ada yang menggunakan GP 2. GP 2 adalah bahasa pemrograman yang mempunyai tingkat komputabilitas yang tinggi dan dapat divisualisasikan dengan mudah. Pada penelitian ini, sistem rekomendasi dibangun dengan GP 2. Secara umum, sistem yang dibangun memberi rekomendasi berdasarkan dua hal, yaitu kemiripan preferensi antar user dan nilai rata-rata suatu film. Dari empat skema rekomendasi yang dibangun, dua di antaranya memberikan akurasi terbaik, yaitu 71%. Namun, salah satunya membutuhkan waktu eksekusi yang jauh lebih singkat dari skema lainnya.

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“…The language has a simple syntax and semantics to support formal reasoning on programs (see [14] for a Hoare-logic approach to verifying graph programs). GP 2's initial implementation is an interpreter running in one of two modes, either fully exploring the non-determinism inherent to transformation rules or attempting to produce a single result [3]. In this paper, we report on a compiler for GP 2 which translates programs directly into efficient C code.…”

Section: Introductionmentioning

confidence: 99%

Compiling Graph Programs to C

Bak

Plump

2016

Graph Transformation

Self Cite

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Abstract. We show how to generate efficient C code for a high-level domain-specific language for graphs. The experimental language GP 2 is based on graph transformation rules and aims to facilitate formal reasoning on programs. Implementing graph programs is challenging because rule matching is expensive in general. GP 2 addresses this problem by providing rooted rules which under mild conditions can be matched in constant time. Using a search plan, our compiler generates C code for matching rooted graph transformation rules. We present run-time experiments with our implementation in a case study on checking graphs for two-colourability: on grid graphs of up to 100,000 nodes, the compiled GP 2 program is as fast as the tailor-made C program given by Sedgewick.

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A Reference Interpreter for the Graph Programming Language GP 2

Cited by 6 publications

References 20 publications

Combining Dynamic and Static Analysis to Help Develop Correct Graph Transformations

Combining Dynamic and Static Analysis to Help Develop Correct Graph Transformations

Pembangunan Knowledge Graph dan Sistem Rekomendasi Film dengan GP 2

Compiling Graph Programs to C

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