Automatic code generation

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AUTOMATIC CODE GENERATION FROM UML DIAGRAMS: THE STATE-OF-THE-ART

AUTOMATIC CODE GENERATION FROM UML DIAGRAMS: THE STATE-OF-THE-ART

The emergence of the Unified Modeling Language (UML) as the de-facto standard for modeling software systems has encouraged the development of automated software tools that facilitate automatic code generation. UML diagrams are used to diagrammatically model and specify the static structure as well as the dynamic behavior of object-oriented systems and the software tools then go ahead and automatically produce code from the given diagrams. In the last two decades substantial work has been done in this area of automatic code generation. This paper is aimed at identifying and classifying this work pertaining to automatic code generation from UML diagrams, restricting the search neither to a specific context nor to a particular programming language. A Systematic literature review (SLR) using the keywords “automatic code generation”, “MDE”, “code generation” and “UML” is used to identify 40 research papers published during the years 2000–2016 which are broadly classified into three groups: Approaches, Frameworks and Tools. For each paper, an analysis is made of the achievements and the gaps, the UML diagrams used the programming languages and the platform. This analysis helps to answer the main questions that the paper addresses including what techniques or implementation methods have been used for automatic code generation from UML Diagrams, what are the achievements and gaps in the field of automatic code generation from UML diagrams, which UML diagram is most used for automatic code generation from UML diagrams, which programming language source code is mostly automatically generated from the design models and which is the most used target platform? The answers provided in this paper will assist researchers, practitioners and developers to know the current state-of-the-art in automatic code generation from UML diagrams.
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Natural Language Programing with Automatic Code Generation towards Solving Addition Subtraction Word Problems

Natural Language Programing with Automatic Code Generation towards Solving Addition Subtraction Word Problems

Solving mathematical word problems by understanding natural language texts and by representing them in the form of equa- tions to generate the final answers has been gaining importance in recent days. At the same time, automatic code genera- tion from natural language text input (nat- ural language programming) in the field of software engineering and natural lan- guage processing (NLP) is drawing the at- tention of researchers. Representing natu- ral language texts consisting of mathemat- ical or logical information into such pro- grammable event driven scenario to find a conclusion has immense effect in auto- matic code generation in software engi- neering, e-learning education, financial re- port generation, etc. In this paper, we pro- pose a model that extracts relevant infor- mation from mathematical word problem (MWP) texts, stores them in predefined templates, models them in object oriented paradigm, and finally map into an object oriented programming (OOP) 1 language
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Visual Composition and Automatic Code Generation for Heterogeneous Components Coordination with Reo

Visual Composition and Automatic Code Generation for Heterogeneous Components Coordination with Reo

Abstract— With the expansion of new technologies such as web-services, most of tools and systems have migrated toward an adaption to this actual context. However legacy components and applications still constitute major part of companies’ system infrastructures. Their use in the actual context is of a particular interest for companies that possess critical infrastructures in production and that do not want to be involved in costly activities in terms of money and time. In this paper, we present a tool that enables the automatic generation of code for the composition and coordination of legacy heterogeneous components with the Reo coordination language. Reo is a channel-based exogenous coordination model that enables the coordination of entities in an effective way. The benefit it offers by its independence from the implementation of the components or their properties and their interactions makes it a good choice for coordination. In this paper, we present an approach based on the integration of the CORBA middleware as a new layer part of the Extensible Coordination Tools (ECT) framework which is a java implementation of the REO language to extend its features to the coordination of heterogeneous components.
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A graphical method for automatic code generation from extended s system petri net models

A graphical method for automatic code generation from extended s system petri net models

Hasil kerja ini juga memperkenalkan suatu prosedur yang memetakan model S-System Petri Net yang dibina dalam diagram editor kepada control flow graphs.. Prosedur ini menggunakan suatu ko[r]

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Large Scale Problem Solving Using Automatic Code Generation and Distributed Visualization

Large Scale Problem Solving Using Automatic Code Generation and Distributed Visualization

Abstract. Scientific computation faces multiple scalability challenges in trying to take advantage of the latest generation compute, network and graphics hardware. We present a comprehensive approach to solving four important scalability challenges: programming productivity, scalability to large numbers of processors, I/O bandwidth, and interactive visualization of large data. We describe a scenario where our integrated system is applied in the field of numerical relativity. A solver for the governing Einstein equations is generated and executed on a large computational cluster; the simulation output is distributed onto a distributed data server, and finally visualized using distributed visualization methods and high-speed networks. A demonstration of this system was awarded first place in the IEEE SCALE 2009 Challenge.
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Automatic PLC code generation based on virtual engineering model

Automatic PLC code generation based on virtual engineering model

The paper demonstrates structured PLC automatic code generation based on virtual engineering model and process planning data available in VueOne toolset. The generated code has been structured in order to be in-line with currently manual programming practices used in industrial applications. The generated code can be monitored and changed by technicians if required (such as bypassing an interlock due to a faulty sensor). Furthermore, it considers simple programming style for easy code tracing, machine monitoring and diagnostics, maintenance activities, and machine future reconfiguration and upgrades activities. This research paper also considers another angle of the PLC code generation, which is connectivity and machine data records. This is an important aspect of smart manufacturing systems, where the PLC needs to be a part of the CPS and provide all required IoT connectivity as well as real time process data to feed it back to the virtual world and create a connected digital twin.
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Automatic Bilingual Caption Generation

Automatic Bilingual Caption Generation

technologies. In our day to day life, most of the time we mix the English language with native language. The system is designed for bilingual subtitle generation for various native languages. The objective of the proposed system is to design and implement an STT conversion system for English to Marathi, Hindi, Gujarati and Bengali languages. The system gives English subtitle along with user preferred native language for the given input video. The system works around a dataset of 1000 English sentences with their respective native language equivalents. This work is based on MFCC and HMM. The outline of the paper is as follows. Section II gives a brief idea of implementation of current systems and their corresponding algorithms. Section III gives the overview of the system along with the various steps and component involved. Section IV describes the audio extraction and feature extraction mechanism used in system for speech recognition. It also explains the classification step done by HMM. Section V is all about the experimental setup based on MFCC and HMM along with result analysis. Section VI concludes the paper. Section VII says about future work.
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Automatic Test Packet Generation

Automatic Test Packet Generation

Networks are getting larger and more complex, yet administrators rely on rudimentary tools such as ping and trace route to debug problems. We propose an automated and systematic approach for testing and debugging networks called “Automatic Test Packet Generation” (ATPG). ATPG reads router configurations and generates a device-independent model. The model is used to generate a minimum set of test packets to (minimally) exercise every link in the network or (maximally) exercise every rule in the network. Test packets are sent periodically, and detected failures trigger a separate mechanism to localize the fault. ATPG can detect both functional (e.g., incorrect firewall rule) and performance problems (e.g., congested queue). ATPG complements but goes beyond earlier work in static checking (which cannot detect liveness or performance faults) or fault localization (which only localize faults given liveness results). We describe our prototype ATPG implementation and results on two real-world data sets: Stanford University’s backbone network and Internet2. We find that a small number of test packets suffice to test all rules in these networks: For example, 4000 packets can cover all rules in Stanford backbone network, while 54 are enough to cover all links. Sending 4000 test packets 10 times per second consume less than 1% of link capacity. ATPG code and the data sets are publicly available.
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Towards Automatic Generation of Natural Language Generation Systems

Towards Automatic Generation of Natural Language Generation Systems

Integration of SPoT as described in Section 2 and FERGUS as described in Section 3 is not automatic. The reason is that the output of SPoT is a deep syntax tree (Mel’ˇ cuk, 1998) whereas hitherto the input of FERGUS has been a surface syntax tree. The primary dis- tinguishing characteristic of a deep syntax tree is that it contains features for categories such as definiteness for nouns, or tense and aspect for verbs. In contrast, a surface syntax tree real- izes these features as function words. However, there is a one-to-one mapping from features of a deep syntax tree to function words in the cor- responding surface syntax tree. Therefore, inte- grating SPoT with FERGUS is basically a mat- ter of performing this mapping. We have added a rule-based component (RB) as the new first stage of FERGUS to do just that. Note that it is erroneous to think that RB makes choices be- tween different generation options because there is a one-to-one mapping between features and function words.
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Automatic Generation of Story Highlights

Automatic Generation of Story Highlights

Svore et al. (2007) were the first to foreground the highlight generation task which we adopt as an evaluation testbed for our model. Their approach is however a purely extractive one. Using an al- gorithm based on neural networks and third-party resources (e.g., news query logs and Wikipedia en- tries) they rank sentences and select the three high- est scoring ones as story highlights. In contrast, we aim to generate rather than extract highlights. As a first step we focus on deleting extraneous ma- terial, but other more sophisticated rewrite opera- tions (e.g., Cohn and Lapata 2009) could be incor- porated into our framework.
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Automatic Generation and Execution of Mutants

Automatic Generation and Execution of Mutants

Mutant Schema Generation: Mutants schema Generation approach designed to reduce the overall cost of traditional interpreter-based techniques. Instead of compiling the each mutant separately, the mutant schema technique generates a metaprogram. This metamutant can be used to represent all possible mutants. Therefore, to run each mutant against the test set, only this metaprogram need to be compiled. Thus the cost of this technique is composed of a one-time compilation cost and the overall run time cost. As this metaprogram is a compiled program its running speed is faster than the interpreter-based technique.
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Automatic Generation of Jokes in Hindi

Automatic Generation of Jokes in Hindi

Our Dur se Dekha joke generator is a first step towards the exploration of humour in Hindi lan- guage generation. In this paper, we took a focused form of humorous tercets in Hindi - Dur se Dekha, and performed an analysis of its structure and hu- mour encoding. We then created a lexicon, and came up with an algorithm to form the various el- ements of the joke following specific constraints. We saw that the jokes generated by our system gave decent results in terms of naturalness and hu- mour to serve as a baseline for future work.

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AUTO GENERATION OF CODE AND TABLE TOOL

AUTO GENERATION OF CODE AND TABLE TOOL

__________________________________________________________________________________________________ Abstract-- The paper is about a windows application of which will generate initial code files, tables schema and stored procedures to deal with the tables generated. The generated codes will be used by the software developers of a company which can be integrated with any web/window application(s). This tool can connect with any database server on network and execute the script generated. In the proposed system, there must exist a database where the user can create table with the required number of fields. Every table created is be placed under the same database. The code can be generated by any top level manager/ project head (with basic knowledge of computers). The tool will even guarantee the level of abstraction to be maintained between the developers hierarchy.
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Automatic Test Packet Generation

Automatic Test Packet Generation

Network administrator use primitive tools such as Ping and traceroute. My survey results indicate they are esager for more sophisticated tools. Other field of engineering indicate that desires are not unreasonable: For example, software design industries are buttressed by billion-dollar tool businesses that supply techniques for both static (e.g., design rule) and dynamic (e.g., timing) verification. In fact, that ATPG was a well- known series of checking hardware chip testing, where it stands for Automatic Test Pattern Generation [4]. We hope network ATPG will be equally useful for automated dynamic testing of production networks.
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Automatic Test Packet Generation

Automatic Test Packet Generation

Automatic Test Packet Generation (ATPG) framework that automatically generates a minimal set of packets to test the liveness of the underlying topology and the congruence between data plane state and configuration specifications. The tool can also automatically generate packets to test performance assertions such as packet latency. It can also be specialized to generate a minimal set of packets that merely test every link for network liveness.

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Latent Predictor Networks for Code Generation

Latent Predictor Networks for Code Generation

Retrieval Baseline It was reported in (Quirk et al., 2015) that a simple retrieval method that out- puts the most similar input for each sample, mea- sured using Levenshtein Distance, leads to good results. We implement this baseline by computing the average Levenshtein Distance for each input field. This baseline is denoted “Retrieval”. Evaluation A typical metric is to compute the accuracy of whether the generated code exactly matches the reference code. This is informative as it gives an intuition of how many samples can be used without further human post-editing. How- ever, it does not provide an illustration on the de- gree of closeness to achieving the correct code. Thus, we also test using BLEU-4 (Papineni et al., 2002) at the token level. There are clearly problems with these metrics. For instance, source code can be correct without matching the refer- ence. The code in Figure 2, could have also been implemented by calling the draw function in an cycle that exists once both players have the same number of cards in their hands. Some tasks, such as the generation of queries (Zelle and Mooney, 1996), have overcome this problem by executing the query and checking if the result is the same as the annotation. However, we shall leave the study of these methologies for future work, as adapting these methods for our tasks is not triv-
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Metamporphic Code Generation Using LLVM

Metamporphic Code Generation Using LLVM

Unexpectedly, the Add Degree 25 and Add Degree 50 sets appear to score better than the original training data. Reviewing Figure 22, we can see the non-linear fashion in which scores improve relative to code added. At very high Add Degrees, the score improves very little, and it takes a significant increase, 433.38%, in program size to evade HMM detection. Additional datasets were evaluated and are provided in Appendix A. These include scoring optimized morphed variants using a model trained with non-optimized data (see A.1), scoring non-optimized variants using a model trained with optimized data (see A.2), and scoring optimized variants using a model trained with optimized data (see A.3).
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Automatic Refactoring for Renamed Clones in Test Code

Automatic Refactoring for Renamed Clones in Test Code

First, we use popular clone detection tools to select refactoring candidates. In our pre- processing work, we have tried some popular tools such as CloneDR [9], Deckard [18], and CCFinder [22]. Finally we decided to use Deckard as our clone detection tool because it is free software that easily works under Linux. It is also a tree-based, accurate, and scalable code clone detection tool, which is easy to install and use. After detecting the clone candidates with the clone detection tool, we use a simple script to extract the clone pairs in the appropriate format from the clone detection report. We save these results in the input file for the following processing. Also, the user is free to modify the input file with a custom selection of clone pairs. Note that our technique can work with other clone detection tools. Any clone detection tool can be used as long as it generates output in the appropriate format.
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Automatic Detection of Intra Word Code Switching

Automatic Detection of Intra Word Code Switching

This study is a first step towards detecting code-switching within words using computational methods, which could support the processing of code-switched texts and support sociolinguists in their study of code-switching patterns. We fo- cus on tweets from a province in the Netherlands where a minority language is spoken alongside Dutch (see Section 3). We automatically segment the words into smaller units using the Morfessor tool (Section 4). We then identify words with sub- units that are associated with different languages (Section 5).

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Automatic Generate CNC Code for Symmetrical Object

Automatic Generate CNC Code for Symmetrical Object

ABSTRACT: This project “AUTOMATIC GENERATE CNC CODE FOR SYMMETRICAL OBJECT” mainly focuses in cloning any symmetrical object from any remote area. First, software is created which is meant to generate CNC codes for lathe automatically and it sends the generated codes to any part of the world with the help of internet in a fraction of second, this also can be said as live object transfer.

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