Popular Programming Languages Research Articles

Evolution of PHP Applications: A Systematic Literature Review

<div class="WordSection1"><p><strong>This paper reviews, some of the research work done in the evolution of PHP applications that have been around and are vastly used. </strong><strong>PHP is currently one of the most popular programming languages, widely used in both the open source community and in industry to build large web-focused applications and application frameworks. This review looks at how PHP applications have evolved in terms of the use of libraries, the software maturity, adoption of object-orientation paradigm, the evolution of complexity and security. The results suggest that these systems undergo systematic maintenance and evolution is helping the underlying programming language to grow.</strong></p></div>

A fast and efficient python library for interfacing with the Biological Magnetic Resonance Data Bank

BackgroundThe Biological Magnetic Resonance Data Bank (BMRB) is a public repository of Nuclear Magnetic Resonance (NMR) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecules. It is primarily maintained and accessed in a flat file ASCII format known as NMR-STAR. While the format is human readable, the size of most BMRB entries makes computer readability and explicit representation a practical requirement for almost any rigorous systematic analysis.ResultsTo aid in the use of this public resource, we have developed a package called nmrstarlib in the popular open-source programming language Python. The nmrstarlib’s implementation is very efficient, both in design and execution. The library has facilities for reading and writing both NMR-STAR version 2.1 and 3.1 formatted files, parsing them into usable Python dictionary- and list-based data structures, making access and manipulation of the experimental data very natural within Python programs (i.e. “saveframe” and “loop” records represented as individual Python dictionary data structures). Another major advantage of this design is that data stored in original NMR-STAR can be easily converted into its equivalent JavaScript Object Notation (JSON) format, a lightweight data interchange format, facilitating data access and manipulation using Python and any other programming language that implements a JSON parser/generator (i.e., all popular programming languages). We have also developed tools to visualize assigned chemical shift values and to convert between NMR-STAR and JSONized NMR-STAR formatted files. Full API Reference Documentation, User Guide and Tutorial with code examples are also available.We have tested this new library on all current BMRB entries: 100% of all entries are parsed without any errors for both NMR-STAR version 2.1 and version 3.1 formatted files. We also compared our software to three currently available Python libraries for parsing NMR-STAR formatted files: PyStarLib, NMRPyStar, and PyNMRSTAR.ConclusionsThe nmrstarlib package is a simple, fast, and efficient library for accessing data from the BMRB. The library provides an intuitive dictionary-based interface with which Python programs can read, edit, and write NMR-STAR formatted files and their equivalent JSONized NMR-STAR files. The nmrstarlib package can be used as a library for accessing and manipulating data stored in NMR-STAR files and as a command-line tool to convert from NMR-STAR file format into its equivalent JSON file format and vice versa, and to visualize chemical shift values. Furthermore, the nmrstarlib implementation provides a guide for effectively JSONizing other older scientific formats, improving the FAIRness of data in these formats.

BuddySuite: Command-Line Toolkits for Manipulating Sequences, Alignments, and Phylogenetic Trees.

The ability to manipulate sequence, alignment, and phylogenetic tree files has become an increasingly important skill in the life sciences, whether to generate summary information or to prepare data for further downstream analysis. The command line can be an extremely powerful environment for interacting with these resources, but only if the user has the appropriate general-purpose tools on hand. BuddySuite is a collection of four independent yet interrelated command-line toolkits that facilitate each step in the workflow of sequence discovery, curation, alignment, and phylogenetic reconstruction. Most common sequence, alignment, and tree file formats are automatically detected and parsed, and over 100 tools have been implemented for manipulating these data. The project has been engineered to easily accommodate the addition of new tools, is written in the popular programming language Python, and is hosted on the Python Package Index and GitHub to maximize accessibility. Documentation for each BuddySuite tool, including usage examples, is available at http://tiny.cc/buddysuite_wiki. All software is open source and freely available through http://research.nhgri.nih.gov/software/BuddySuite.

It's Time for a New Old Language

The most popular programming language in computer science has no compiler or interpreter. Its definition is not written down in any one place. It has changed a lot over the decades, and those changes have introduced ambiguities and inconsistencies. Today, dozens of variations are in use, and its complexity has reached the point where it needs to be re-explained, at least in part, every time it is used. Much effort has been spent in hand-translating between this language and other languages that do have compilers. The language is quite amenable to parallel computation, but this fact has gone unexploited. In this talk we will summarize the history of the language, highlight the variations and some of the problems that have arisen, and propose specific solutions. We suggest that it is high time that this language be given a complete formal specification, and that compilers, IDEs, and proof-checkers be created to support it, so that all the best tools and techniques of our trade may be applied to it also.

Enhanced Code Conversion Approach for the Integrated Cross-Platform Mobile Development (ICPMD)

Mobile development companies aim to maximize the return on investments by making their mobile applications (Apps) available on different mobile platforms. Consequently, the same App is developed several times; each time the developer uses the programming languages and development tools of a specific platform. Therefore, there is a need to have cross-platform mobile applications development solutions that enable the developers to develop the App once and run it everywhere. The Integrated Cross-Platform Mobile Applications Development (ICPMD) solution is one of the attempts that enables the developers to use the most popular programming languages like Java for Android and C# for Windows Phone 8 (WP8). ICPMD is used to transform both the source code and user interface to another language to generate full Apps on the target platform. This paper extends ICPMD by proposing a new code conversion approach based on XSLT and Regular Expressions to ease the conversion process. In addition, it provides the assessment method to compare the ICPMD efficiency with competing approaches. Several Apps are converted from WP8 to Android and vice versa. The ICPMD evaluation results show reasonable improvement over commercial cross-platform mobile development tools (Titanium and Xamarin).

Automatic non-functional testing of code generators families

The intensive use of generative programming techniques provides an elegant engineering solution to deal with the heterogeneity of platforms and technological stacks. The use of domain-specific languages for example, leads to the creation of numerous code generators that automatically translate highlevel system specifications into multi-target executable code. Producing correct and efficient code generator is complex and error-prone. Although software designers provide generally high-level test suites to verify the functional outcome of generated code, it remains challenging and tedious to verify the behavior of produced code in terms of non-functional properties. This paper describes a practical approach based on a runtime monitoring infrastructure to automatically check the potential inefficient code generators. This infrastructure, based on system containers as execution platforms, allows code-generator developers to evaluate the generated code performance. We evaluate our approach by analyzing the performance of Haxe, a popular high-level programming language that involves a set of cross-platform code generators. Experimental results show that our approach is able to detect some performance inconsistencies that reveal real issues in Haxe code generators.

GEMs: shared-memory parallel programming for Node.js

JavaScript is the most popular programming language for client-side Web applications, and Node.js has popularized the language for server-side computing, too. In this domain, the minimal support for parallel programming remains however a major limitation. In this paper we introduce a novel parallel programming abstraction called Generic Messages (GEMs). GEMs allow one to combine message passing and shared-memory parallelism, extending the classes of parallel applications that can be built with Node.js. GEMs have customizable semantics and enable several forms of thread safety, isolation, and concurrency control. GEMs are designed as convenient JavaScript abstractions that expose high-level and safe parallelism models to the developer. Experiments show that GEMs outperform equivalent Node.js applications thanks to their usage of shared memory.

A Proposed Software as a Service (SaaS) Toolkit for Cloud Multi- Tenancy

Cloud computing is a general term for anything that involves delivering hosted services over the Internet. It is a construct that allows you to access applications that actually reside at locations other than your computer or other Internet-connected devices. Software as a Service (SaaS) is a software delivery model in which software resources are accessed remotely by users over the internet. Enterprises prefer using SaaS because of its low cost. SaaS requires sharing of application servers among multiple tenants for low operational costs. Several SaaS Toolkits are available and used to develop cloud SaaS applications. However, most of these Toolkits are very complex. This paper aims to build a new simple and easy SaaS Toolkit to develop SaaS applications and to avoid the difficulties and complexity in the current cloud SaaS Toolkits. The proposed Toolkit is based on java virtual machine and the popular web programming languages HTML and PHP. To evaluate the proposed Toolkit an empirical study has been conducted. The result of the empirical study revealed that the proposed Toolkit outperforms the current Toolkits in terms of complexity, understandability and learnability.

An Introduction to Programming for Bioscientists: A Python-Based Primer.

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in molecular biology, biochemistry, and other biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language’s usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a “variable,” the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.

Enabling PHP software engineering research in Rascal

Today, PHP is one of the most popular programming languages, and is commonly used in the open source community and in industry to build large application frameworks and web applications. In this paper, we discuss our ongoing work on PHP AiR, a framework for PHP Analysis in Rascal. PHP AiR is focused especially on program analysis and empirical software engineering, and is being used actively and effectively in work on evaluating PHP feature usage and system evolution, on program analysis for refactoring and security validation, and on source code metrics. We describe the requirements and design decisions for PHP AiR, summarize current research using PHP AiR, discuss lessons learned, and briefly sketch future work. • We describe a framework in Rascal for PHP software engineering research, PHP AiR. • We report results of using PHP AiR for empirical studies and program analysis. • We examine the design decisions and tradeoffs encountered building PHP AiR.

Classification of gene expression data: A hubness-aware semi-supervised approach

Background and objectiveClassification of gene expression data is the common denominator of various biomedical recognition tasks. However, obtaining class labels for large training samples may be difficult or even impossible in many cases. Therefore, semi-supervised classification techniques are required as semi-supervised classifiers take advantage of unlabeled data. MethodsGene expression data is high-dimensional which gives rise to the phenomena known under the umbrella of the curse of dimensionality, one of its recently explored aspects being the presence of hubs or hubness for short. Therefore, hubness-aware classifiers have been developed recently, such as Naive Hubness-Bayesian k-Nearest Neighbor (NHBNN). In this paper, we propose a semi-supervised extension of NHBNN which follows the self-training schema. As one of the core components of self-training is the certainty score, we propose a new hubness-aware certainty score. ResultsWe performed experiments on publicly available gene expression data. These experiments show that the proposed classifier outperforms its competitors. We investigated the impact of each of the components (classification algorithm, semi-supervised technique, hubness-aware certainty score) separately and showed that each of these components are relevant to the performance of the proposed approach. ConclusionsOur results imply that our approach may increase classification accuracy and reduce computational costs (i.e., runtime). Based on the promising results presented in the paper, we envision that hubness-aware techniques will be used in various other biomedical machine learning tasks. In order to accelerate this process, we made an implementation of hubness-aware machine learning techniques publicly available in the PyHubs software package (http://www.biointelligence.hu/pyhubs) implemented in Python, one of the most popular programming languages of data science.

Методы оптимизации программ на языке JavaScript, основанные на статистике выполнения программы

In recent years, JavaScript has become one of the most popular programming languages on the web. Many massive applications are written using JavaScript, such as Gmail, Google docs, etc. JavaScript is also used in the Node.js - server-side web application developing platform. Moreover, JavaScript is the main language for developing applications on some operating systems for mobile and media devices. Examples of such systems are Tizen and FirefoxOS. Due to increasing popularity of JavaScript many big companies produced and continue to develop their own dynamic compilers for this language. JIT compilation makes it possible to implement many well-known classic optimizations to improve programs performance. To maintain a trade-off between quick startup and doing sophisticated optimizations, JavaScript engines usually use multiple tiers for compiling hot functions: lower tier JITs generate less efficient code, but can start almost immediately (e.g., even with interpretation), while higher tier JITs aim at generating very effective code for hot places, but at the cost of long compilation time. So even highly optimized JavaScript execution engines require some time to "warm-up" before reaching their peak performance. This work is dedicated to the performance improvement of modern dynamic multitier JIT compilers by designing and implementing profile-based optimizations in JavaScript V8 JIT compiler.

E-learning of PHP based on the solutions of real-life problems

PHP is one of the most popular web programming languages. Many people want to learn PHP because of its simplicity. One of the important features of PHP is that it is a server-side scripting language. Learning PHP is more effective if a student can learn through a real environment. Many PHP online-learning systems and hardcopy books are available at present. Problem-based learning (PBL) is an effective pedagogy used to increase the problem-solving skill of the students. Existing PHP learning systems support limited problems in real-life environment. In this work, we have integrated the conventional e-learning of PHP with the PBL pedagogy to improve the problem-solving capability of the students. In this system, a teacher designs some problems for the students. Each problem contains proper description and reference with standard PHP output. On the web server, students can practice and submit answers pertaining to the problem with the help of the relevant references, urls, examples, and standard output. System provides the facility for the students to practice and experiment on coding with real-time error. Students can edit codes repeatedly comparing their outputs with the standard output within the specific duration. For evaluation, a teacher can compare the students’ submitted codes with the standard code of the system, predefined keywords, and outputs. This system helps the students to engage in self-learning of PHP based on the real problems.

Optimal Motion Planning Approach for Mobile Robot Navigation Problem

In this study, modified genetic algorithm (MGA) and A* search method (A*) is proposed for optimal motion planning of mobile robots. MGA utilizes the classical search and modified A* to establish a sub-optimal collision-free path as initial solution in simple and complex static environment. The enhancements for the proposed approach are presented in initialization stage and enhanced operators. Five objective functions are used to minimize traveling length, time, smoothness, security and trajectory and to reduce the energy consumption for mobile robots by using Cubic Spline interpolation curve fitting for optimal planned path. The purpose of this study is to evaluate the proposed approach performance by taking into consideration the effect of changing the number of iteration (it) and the size of population (pop) on its performance index. The simulation results show the effectiveness of proposed approach in governing the robot’s movements successfully from start to goal point after avoiding all obstacles its way in all tested environment. In addition, the results indicate that the proposed approach can find the optimal solution efficiently in a single run. This approach has been carried out by GUI using a popular engineering programming language, MATLAB.

Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM

Support Vector Machines (SVM) is one of machine learning methods that can be used to perform classification task. Many researchers using SVM library to accelerate their research development. Using such a library will save their time and avoid to write codes from scratch. LibSVM is one of SVM library that has been widely used by researchers to solve their problems. The library also integrated to WEKA, one of popular Data Mining tools. This article contain results of our work related to complexity analysis of Support Vector Machines. Our work has focus on SVM algorithm and its implementation in LibSVM. We also using two popular programming languages i.e C++ and Java with three different dataset to test our analysis and experiment. The results of our research has proved that the complexity of SVM (LibSVM) is O(n3) and the time complexity shown that C++ faster than Java, both in training and testing, beside that the data growth will be affect and increase the time of computation.

Review of Methodological Primitives for the Phased Construction of Data Visualization Model

The paper considers common methodological primitives by the example of phased data visualization model construction with prepared data. The purpose of the article is to form a comprehensive vision of the visualization model creation. The primitives classification and communication between them are suggested. The paper also considers data visualization libraries in popular programming languages.

Towards Enabling Overture as a Platform for Formal Notation IDEs

Formal Methods tools will never have as many users as tools for popular programming languages and so the effort spent on constructing Integrated Development Environments (IDEs) will be orders of magnitudes lower than that of programming languages such as Java. This means newcomers to formal methods do not get the same user experience as with their favourite programming IDE. In order to improve this situation it is essential that efforts are combined so it is possible to reuse common features and thus not start from scratch every time. This paper presents the Overture platform where such a reuse philosophy is present. We give an overview of the platform itself as well as the extensibility principles that enable much of the reuse. The paper also contains several examples platform extensions, both in the form of new features and a new IDE supporting a new language.

HRF-Relaxed

Memory consistency models, or memory models, allow both programmers and program language implementers to reason about concurrent accesses to one or more memory locations. Memory model specifications balance the often conflicting needs for precise semantics, implementation flexibility, and ease of understanding. Toward that end, popular programming languages like Java, C, and C++ have adopted memory models built on the conceptual foundation of Sequential Consistency for Data-Race-Free programs (SC for DRF). These SC for DRF languages were created with general-purpose homogeneous CPU systems in mind, and all assume a single, global memory address space. Such a uniform address space is usually power and performance prohibitive in heterogeneous Systems on Chips (SoCs), and for that reason most heterogeneous languages have adopted split address spaces and operations with nonglobal visibility. There have recently been two attempts to bridge the disconnect between the CPU-centric assumptions of the SC for DRF framework and the realities of heterogeneous SoC architectures. Hower et al. proposed a class of Heterogeneous-Race-Free (HRF) memory models that provide a foundation for understanding many of the issues in heterogeneous memory models. At the same time, the Khronos Group developed the OpenCL 2.0 memory model that builds on the C++ memory model. The OpenCL 2.0 model includes features not addressed by HRF: primarily support for relaxed atomics and a property referred to as scope inclusion. In this article, we generalize HRF to allow formalization of and reasoning about more complicated models using OpenCL 2.0 as a point of reference. With that generalization, we (1) make the OpenCL 2.0 memory model more accessible by introducing a platform for feature comparisons to other models, (2) consider a number of shortcomings in the current OpenCL 2.0 model, and (3) propose changes that could be adopted by future OpenCL 2.0 revisions or by other, related, models.

Algorithm Differentiation in Lisp

Algorithm differentiation (AD) is a technique used to transform a program F computing a numerical function of one argument F(x) into another program G(p) that returns a pair, hF(p), F0(p)i where by F0(p) we mean the derivative of F with respect to its argument x, evaluated at x = p. That is, we have a program AD that takes as input a program, and returns another: G := AD(F). Over the years AD programs have been developed to allow F to be expressed in some specialized variant of a popular programming language L (FORTRAN, C, Matlab, Python) and where G is delivered in that language L or some other. Alternatively, executing F(p) is some environment will deliver hF0(p), F(p)i directly. AD tools have also been incorporated in computer algebra systems (CAS) such as Maple. A CAS is hardly necessary for the task of writing the AD program, since the main requirement is a set of tools for manipulation of an internal (typically tree) form of a program. In Lisp, a normal program is already in this form and so the AD program in Lisp (ADIL), the target F and the product G can all be expressed compactly in Lisp. In spite of the brevity and extensibility of the ADIL program, we can provide features which are unsupported in other AD programs. In particular, recursive functions are easily accommodated. Our perspective here is to point out that for scientists who write programs in Lisp or any language that can be converted to Lisp, AD is easily at hand.

Guido van Rossum: The Early Years of Python

Guido van Rossum discusses the initial development of Python, which has increasingly become the programming language of choice for many scientific fields due to its extensibility and ease of use. Combining simplicity and ease of learning with powerful capabilities and strong performance, Python is one of the most popular programming languages in the world. Created by Guido van Rossum in 1989, Python is the leading application language in important emerging areas such as natural language processing and big data. It fills a very important gap between high-level applications like spreadsheets and statistical analysis packages and systems-oriented languages like C, C++, and Java. You can see my full interview with Guido at www.computer.org/computingconversations.