Code clones defined as sequence of source code that occur more than once in the same program or across different programs are undesirable as they increase the size of program and creates the problems of redundancy. Fixing of bugs detected in one clone require detection of all clones. Hence, it is imperative to identify and remove all code clones in a program. The focus of previous research work on the code clone detection was to find identical clones, or clones that are identical up to identifiers and literal values. But, detection of similar clones is often important. In the present paper it is proposed to generate the feature sets after parsing the given C program for code fragments and then match their similarity. On the basis of feature sets the classification of algorithm is being performed by using the Support Vector Machine (SVM) as a machine learning tool. The output of the machine tool would be the similarity ratio with which the two C programs are related to each other and also the class in which they would occur. It was observed that the test results of the tool implementation show detection of code clones in the program and its accuracy increases with the increase in number of instances.

Code clones detection using machine learning technique: Support vector machine

Information Present in Different language and Structure gives Rise to language as barrier in information retrieval. Informative Document on queen Elizabeth is been writing by foreign language English which makes its difficult for a Marathi reader to understand and seek History of England, on Similar lines Literature Work on Shivaji is mostly documented in Marathi which makes foreign Historians difficult to gain know, in both case user is at times unknown of facts due to language and may lose interest on information. Vital information on current happing on village and taluka level are been published in newspaper with local language which setback information spread among other Masses. Many Time government documents and forms are been presented in English Language where a lay man from Marathi language background finds difficulty to understand information and even avoid such procedures therefore it highly urges for need of automated Software based Translation system which would assist in cross Domain information Retrieval. Machine Translation assist to translate Information presented in one language to other language. Information can be present in form of text, speech and image translating this information helps for sharing of information and ultimately information gain. A lot of work has been done on Translation of English to Hindi, Tamil Bangla and other foreign languages also. Machine Translation is challenging Research Area with numerous issues due to language ambiguity like grammar, Structure and even fluency of use. Numerous Methodologies have been proposed and developed which have uplifts and downfalls also, Although statistical and rule based at core with each having limitations. rule based produce accurate mapped translation and are trainable system but costly, whereas statistical produce fluent translation but lack accuracy and sense. Hybrid is combine approach which integrated approach and helps to optimize translation output. The research manuscript we present hybrid machine Translator for English to Marathi language which translated Web pages, text Documents on Agriculture (crops fruits for farmer), Medical reports in Marathi and tourism related information. Proposed System consists of Parallel Multi-Engines which process statistical and rule based Translation for same input document and produce a optimized result by performing statistical over rule based which give fluent language sense outputs. Mapper algorithm is been used in rule based Translation, with Agriculture corpus, medical and tourism corpus for statistical evaluation. Marathi wordnet has been implemented to enhance dictionary and incorporate better translation result Currently System has been proposed for text Document which can be extended to speech and voice. Comparative analysis for point view in one dimension of only limited set of Queries is done with Google Translator. Holding hybrid approach as better methodology. A Systematic survey of only 10 key articles used in research has been done. This research article is extension of our previous research surveys and partial implementations. And innovative S-measure has been new parameter proposed and evaluated by our research team.

Hybrid machine translation for English to Marathi: A research evaluation in Machine Translation: (Hybrid translator)

In this paper, we have discussed several code replication detection methods and tools in different dimensions. This review has provided an extensive survey codec clone detection techniques and tools. Starting from clone perceptions, classification of clones and an overall assortment of selected techniques and tools is discussed. This paper covers the whole paradigm in clone detection and presents open research avenues in code clone detection.

Various Code Clone Detection Techniques and Tools: A Comprehensive Survey

Code clones are easy and quick way to add some existing logic from one section to another section. Code clones however increase risk of bug multiplication with each copy of duplicate code if there is a bug in source of clone. Clone is a persistent form of software reuses that effect on maintenance of large software. In previous research, the researchers emphasize on detecting type 1, type 2, type 3 and type 4 type of clones. The existing code clone detection techniques are available like text based, token based, abstract syntax tree, program dependency graph and metric based technique are used to detect clone in source code. In this research, the enhancement in code clone detection algorithm has been proposed which detects code clones by HYBRID approach that is combination of program dependency graph and Metric based clone detection techniques. In this work, firstly implementation of code clone detection will be done by hybrid approach on various datasets. Then, comparison of existing technique will be done with the hybrid technique in terms of achieving enhancement in performance, efficiency and accuracy in results. This method is considered to be the least complex and is to provide a most accurate and efficient way of Clone Detection. The results obtained have been compared with an existing tool for the open source of web applications.

To enhance the code clone detection algorithm by using hybrid approach for detection of code clones

File System is Data-Structure with Logic (method) to handle efficiently a group of information on disk. File systems come are classified according to their structure method of operation, speed of operation, scalability and flexibility size and security. A file system framework is an intends to sort out data anticipated that would be retained after a program terminates by giving methodology to store, retrieve and update information, and also deal with the accessible space on the device(s) which contain it. A file system framework sorts out information in a proficient way and is tuned to the particular attributes of the device. Individual device partitions can be setup utilizing one of the wide ranges of file systems. Each has its own particular features, impediments, and unique characteristics. This paper concentrates on the components of versatile and adaptable parallel file system framework named Lustre. A null hypothesis work has been presented on Luster file system which is a step in HPC(high performance computing). The hypothesis accepts research scope from survey of articles from various portals and presents in better approach developed by our team in research domain of file system to handle issues and built a better scalable file system like luster. A sequential survey analysis has been done on file system for articles from last three decades in File system research. The research scope unwrapped by scholars is been highlighted in this article and implementation work of innovative scalable file system is been presents which solves many research Questions and space in file system research domain. Performance evaluation helps in assisting File system in current scenario. A systematic literature survey has been incorporated in manuscript for finding research directions. Twenty research articles have ben breakdown in abstract methodology and research scope to find in challenges and subsequent methods to overcome those challenges.

Luster a scalable architecture file system: A research implementation on active storage array framework with Luster file system

Search Engines are based on bivalent logic and probability theory with lack of real world conceptual knowledge, higher precision and reasoning ability. Ranked links or snippets are less effective with big data Analysis and web of information. State of art Search Engine Google endeavor to retrieve good quality document, Msn Engine i.e. Bing endeavor decision boosting. Advanced algorithms account in user intents semantics and societal patterns on Web with recommendation system as product (recommendation Engines). Search engines have advanced from Text based to voice based (Dialogue based) to Image based (multimedia as input Question to search). Major advancement analysis of search Engine Enhancement, search Engines have advanced from traditional data base retrieval machine to web based machines from horizontal engines to Vertical Engines (Naukari.com, Quickr.com) with dedicated crawler Technology at heart. Information of web is structured instructed and posses a huge challenge to big data analytics. All though Retrieval results are optimistic yet they Lack in ability to interpret user Question. Precise answering from relevant Informatics is Search Engine Enhancement. Time complexity and memory are Algorithmic and Machine design parameters that support optimized search result. Question Answering Search Engine (QA Engine) is machine with deductive reasoning capability ability to amalgamate information from various knowledge datasets. QA Engine is front linear area in advanced information retrieval techniques, state of art technology to future of search engines, expert systems. QA search engines have advanced from Shallow Technique (keyword technique) to template based Structured Knowledge processing Engines, profile based engines, and context based machines to cross language machines to Multimedia QA Engines. Community based QA Yahoo answer, stack overflow to specialized search engines like ask.com, qura.com, true knowledge. Question Answering system have been embedded in Google Search Engine(Google Question Answering). IBM's Watson, a cognitive machine Thinking machine like Humans is decision support Engine developed under Deep QA project with advanced Natural language processing Information Retrieval with deep mining, a machine learning model that learns over time with reasoning model at base. With advent of android platform web based services like Apple Siri, Android Assistant questioner technology is art that assists. Enhancement future search engines with analysis (thinking), cognitive ability. This manuscript I present in Architecture on Question answering Search Engine that is search Engine Enhancement with Finite State Machine which facilitate answering question to time complexity The abstract, methodology, algorithmic analysis, of 20 research paper facilitate a future research and integration of proposed Architecture of Question Answering Search Engine.

Question Answering Search engine short review and road-map to future QA Search Engine

Code cloning is the code fragment similar to one another in the form of semantics and syntax. In the software development process, it is reuse approach of existing code. While developing a new software version, all the software modules may not be altered or redevelop. Some existing modules are copied with or without modification introducing to the generation of code clones. This is done for saving the developers efforts and time. But there is an issue, if a bug is discovered in some code fragment, then such contents must be discovered by means of clone detectors, to avoid inclusion of such errors or bugs. Clones are made due to reuse approach and programming approach. Reuse approach contain simple reuse by copy/paste activity of design, functionalities and logic. Programming approach involves merging of two similar systems, system development with generative programming approach and delay in restructuring. Code is developed on distributed system. It is difficult to find out code clones on these systems. We need an effective way to detect the clones. Earlier research and tools developed till now can find only Type-I, Type-II and some part of Type-III clones. Detection of Type-IV clone estimates a challenge in current scenario. Some tools are very slow and take lot of time for comparing codes and also their precision is low. Thus they are limited to Type-I and Type-II clones. The tools oriented on PDG are able to find Type-III clones. Time required to detect the clones by existing tools is high due to large number of comparisons. This sets the basis as a metaphor for our current research. The motive of our approach is to reduce comparisons and improve precision. Our proposed method detects duplicate code in efficient way by using Decentralized Computing and Code reduction. Significant efforts are applied to address Type-III and various other clones that set challenging aspects in the research.

Code clone detection using decentralized architecture and code reduction

Arched type Swing in loom of information retrieval system is observed with record progression to information fetch, to knowledge data processing, to intelligent information progression. Subsequent processing machines like document retrieval, text summarization, search engines, rule based machines, expert systems have been developed. These machines have dedicated performance with retrieval measure in particular dimension. Machine learning methods have facilitated reasoning machine with ability like humans. Still a corner in research argues highly intelligent time constraint fact seeking real world information processing machine. Hybrid technology is integration of optimized approaches at various levels of information processing. We proposed a hybrid search answer machine with four techniques of optimization “question reformulation” (from user-intent, profile), “search method” (semantic concept, context, machine learning), “answer presentation” (ranking algorithm), “decision support ” (comparative analysis to choose best techniques to retrieve results). Data corpus is heart of any IR system large dataset facilitates good search which argue to distributed data and computing. Intelligence is reformation proceeds that excel our time and dataset. The machine is designed to facilitated updatable training dataset for fact seeking knowledge acquirement “it trains over data”. Muti-agent model distributed search methodology is proposed. In precise Hybrid extraction of “hybrid models” is performed. Semantic context (concept based) user profiled; best machine learning, decision supportive multiagent distributed search system is proposed. This paper gives underlying technologies overview, with examinations of 30 papers is done as with recent review of technology advancement. The review outcomes are orderly placed with 3 research query answering. The outputs of query structure a trail to search engine answering machine. We facilitate research done by scholars on technology perspective we integrate them to draw a sketch of hybrid search answering. In domain “a point of reference” concepts of research are studied, with comparative views on advance in IR. We identify the benchmark of research methods blueprint and explore space of research in area of intelligent machine implementation.

Hybrid intelligent trail to search engine answering machine: Squat appraisal on pedestal technology (hybrid search machine)

Software based clone detection is in hype as industries demand to such product has risen. Due to code replication means the copy and paste activities, such pattern is recurrent thereby developers can reduce effort and time of rewriting similar code fragment from scratch. In the industrial software system, code replication is found a serious trouble because it may affect on quality, consistency, maintainability and comprehensibility. Thus, efficient approach is needed to detect such replication in distributed environment. The trial here is variety of syntax, compiler dependent language, and various coding styles to solve a single problem. As per the related survey, researchers are finding difficult to evolve code copies, even on regressive benchmarking. The existing software tools have some restrictions to detect perfect code clone. Each software developer may think in different way for the implementation of the same problem. The methodology explained here is to specify an efficient way to detect code clone which is a hybrid model that covers maximum coding behavior and classes of clones. Along with similarity check, the paper describes the importance of dissimilarity detection. Detecting dissimilarity is due to operator or function overloading. Since this is essential feature of a good Object oriented Language. It also discusses key techniques that save time in retrieval and comparison of data, by extracting and arranging code that is mined from code document. The proposed system eliminates efforts of comparing the code line by line between two files, which was followed in traditional algorithm. It defines a reduction technique and code complexity based analysis which increases the probability of success. The concluding mark is that no single scheme defines procedure for all types of clone's detection. In this paper, we introduce a multi-model learning technique to detect various types of code clone, which has been taken up as problem statement in this research work.

An effective approach using dissimilarity measures to estimate software code clone

In the era of web new information is upcoming day by day Researches add their work for their research domains Detecting of originality of research work is in hype In Academic sector students researchers bring in innovative ideas, algorithms stating that their work outperforms prior research They may implement NULL Hypothesis or alternative Hypothesis, detecting their effort is a challenge By means of plagiarism detectors such academic efforts can be evaluated or graded This reflects the essence of research in the field of Plagiarized content detection and grading Some of our research issue highlights to technical scenario to design an algorithm which is adaptable to changing nature of dataset The dataset grows, as new research work is added in due course of time Data extraction from unstructured information is challenging, as no standard pattern is yet defined Such patterns vary from research to research and are domain specific A document in question ie plagiarized or not? Is a join of one or more sentences that originate by the authors research or referenced from previous publications Authors to prove originality use paraphrasing which may have semantic similarity, also some of the contents act as metaphor for upcoming research work It is complex task point out such an activity Methodology states that a document in question is a join of sentences, whereas each sentence is a join of terms Thus we conclude by fork and join operations; plagiarism detection is possible in effective way Document in question is split to produce a sentence vector A term vector is generated by forking sentence to terms for each sentence in sentence vector Mapper is implemented that maps term to sentence and sentence to source document To enhance the accuracy of the model a Multi Agent Based System MAS frame is recommended to adapt varying similarity functions Achieve parallelism in system and adaptability of new similarity measures as well remove one which are not suitable any more to the task

Sachin V. Shinde

Papers

Question Answering Search engine short review and road-map to future QA Search Engine

Code clone detection using decentralized architecture and code reduction

Hybrid intelligent trail to search engine answering machine: Squat appraisal on pedestal technology (hybrid search machine)

An effective approach using dissimilarity measures to estimate software code clone

MAS a scalable framework for research effort evaluation by unsupervised machine learning-Hybrid plagiarism model