Neural Machine Translation for Code Generation

Neural machine translation (NMT) methods developed for natural language processing have been shown to be highly successful in automating translation from one natural language to another. Recently, these NMT methods have been adapted to the generation of program code. In NMT for code generation, the task is to generate output source code that satisfies constraints expressed in the input. In the literature, a variety of different input scenarios have been explored, including generating code based on natural language description, lower-level representations such as binary or assembly (neural decompilation), partial representations of source code (code completion and repair), and source code in another language (code translation). In this paper we survey the NMT for code generation literature, cataloging the variety of methods that have been explored according to input and output representations, model architectures, optimization techniques used, data sets, and evaluation methods. We discuss the limitations of existing methods and future research directionsComment: 33 pages, 1 figur

Kemahiran pemikiran komputasional pelajar melalui modul pembelajaran berasaskan teknologi internet pelbagai benda

kemahiran pemikiran komputasional pelajar, ke arah lebih kreatif dan kritis melalui penggunaan Modul Pembelajaran Berasaskan Teknologi Internet Pelbagai Benda (MP-IoT) yang telah dibangunkan oleh penyelidik. Pembangunan MP-IoT mengikut Model ADDIE dan melibatkan Teknologi Arduino yang diterapkan dalam 5 aktiviti pembelajaran secara amali. Kajian berbentuk kuantitatif jenis kuasi-eksperimental ini telah dijalankan ke atas 52 orang pelajar Tingkatan 4 dari 2 buah sekolah di daerah Batu Pahat, Johor dan Kuala Kangsar, Perak. Data pula telah dianalisis secara deskriptif dan inferensi. Satu set ujian pencapaian pra dan pasca sebagai instrument telah dibangunkan. Analisis Item Indeks Kesukaran (IK), Indeks Diskriminasi, serta Interprestasi skor bagi nilai Alpha Cronbach telah digunakan bagi memastikan soalan ujian pencapaian sesuai digunakan. Manakala dalam proses pembangunan modul MP-IoT, seramai 6 orang guru dari mata pelajaran Sains Komputer dipilih sebagai pakar untuk mengenal pasti kesesuaian dari segi format, kandungan dan kebolehgunaan modul yang dibangunkan Skala Likert lima mata digunakan dalam kajian ini. Secara keseluruhannya, dapatan kajian menggunakan ujian-T sampel berpasangan, menunjukkan terdapat perbezaan yang signifikan terhadap tahap pencapaian pelajar kumpulan kawalan yang didedahkan dengan kaedah konvensional dengan kumpulan rawatan yang didedahkan dengan modul MPIoT, dengan nilai p-value adalah .000 iaitu kurang dari .05 (p<0.05). Selain itu, tahap kemahiran pemikiran komputasional pelajar juga meningkat setelah didedahkan dengan modul MP-IoT

An automated cloud-based big data analytics platform for customer insights

Product reviews have a significant influence on strategic decisions for both businesses and customers on what to produce or buy. However, with the availability of large amounts of online information, manual analysis of reviews is costly and time consuming, as well as being subjective and prone to error. In this work, we present an automated scalable cloud-based system to harness big customer reviews on products for customer insights through data pipeline from data acquisition, analysis to visualisation in an efficient way. The experimental evaluation has shown that the proposed system achieves good performance in terms of accuracy and computing time

BLAST: a Loosely Schema-aware Meta-blocking Approach for Entity Resolution

Identifying records that refer to the same entity is a fundamental step for data integration. Since it is prohibitively expensive to compare every pair of records, blocking techniques are typically employed to reduce the complexity of this task. These techniques partition records into blocks and limit the comparison to records co-occurring in a block. Generally, to deal with highly heterogeneous and noisy data (e.g. semi-structured data of the Web), these techniques rely on redundancy to reduce the chance of missing matches. Meta-blocking is the task of restructuring blocks generated by redundancy-based blocking techniques, removing superfluous comparisons. Existing meta-blocking approaches rely exclusively on schema-agnostic features. In this paper, we demonstrate how “loose” schema information (i.e., statistics collected directly from the data) can be exploited to enhance the quality of the blocks in a holistic loosely schema-aware (meta-)blocking approach that can be used to speed up your favorite Entity Resolution algorithm. We call it Blast (Blocking with Loosely-Aware Schema Techniques). We show how Blast can automatically extract this loose information by adopting a LSH-based step for e ciently scaling to large datasets. We experimentally demonstrate, on real-world datasets, how Blast outperforms the state-of-the-art unsupervised meta-blocking approaches, and, in many cases, also the supervised one

Source Code Generation from Descriptions in a Natural Language

Tato diplomová práce představuje CodeFormer, nový model neuronové sítě, schopný na základě popisu úlohy v anglickém jazyce generovat funkce v programovacím jazyce Python. Tento model, založený na architektuře modelu BART, je předtrénovaný na 230 milionech funkcích získaných z veřejných GitHub repozitářů. Po dotrénování na CodeSearchNet datasetu náš model překonává konkurenční modely a nastavuje tak nové state of the art s 46,12 BLEU, což představuje zlepšení o 13,86 BLEU. Vedle CodeFormer modelu tato práce představuje nový Stack Overflow Code Generation Dataset (SOCGD), který je určený k trénování generativních modelů zdrojových kódů. Na tomto datasetu náš model dosahuje výsledku 47,68 BLEU. Výsledný model lze integrovat do vývojových prostředí a umožnit tak programátorům generovat části zdrojových kódů s cílem zvýšit efektivitu jejich práce. V rámci našeho výzkumu jsme také objevili lepší přístup k trénování modelu BART na úloze strojového překladu. Použitelnost tohoto přístupu na jiných doménách je třeba ověřit v navazující práci.ObhájenoThis work introduces CodeFormer, a Python source code generator pre-trained on a massive GitHub crawl consisting of 230M Python functions. The released model, built on BART architecture, generates Python functions based on descriptions in English. On a CodeSearchNet dataset, the CodeFormer sets a new state of the art with 46.12 BLEU, representing an improvement of 13.86 BLEU. We also release a new parallel corpus for code generation called Stack Overflow Code Generation Dataset (SOCGD), on which our model sets a baseline of 47.68 BLEU. The resulting model is ready to be integrated into a source code suggestion system in an IDE, where it can improve software developers' productivity. During our research, we discovered a better way of training the BART for machine translation. However, the applicability of our approach to other domains must be verified in subsequent work