Understanding the Syntactic Rule Usage in Java
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S-Algol Reference Manual Ron Morrison
S-algol Reference Manual Ron Morrison University of St. Andrews, North Haugh, Fife, Scotland. KY16 9SS CS/79/1 1 Contents Chapter 1. Preface 2. Syntax Specification 3. Types and Type Rules 3.1 Universe of Discourse 3.2 Type Rules 4. Literals 4.1 Integer Literals 4.2 Real Literals 4.3 Boolean Literals 4.4 String Literals 4.5 Pixel Literals 4.6 File Literal 4.7 pntr Literal 5. Primitive Expressions and Operators 5.1 Boolean Expressions 5.2 Comparison Operators 5.3 Arithmetic Expressions 5.4 Arithmetic Precedence Rules 5.5 String Expressions 5.6 Picture Expressions 5.7 Pixel Expressions 5.8 Precedence Table 5.9 Other Expressions 6. Declarations 6.1 Identifiers 6.2 Variables, Constants and Declaration of Data Objects 6.3 Sequences 6.4 Brackets 6.5 Scope Rules 7. Clauses 7.1 Assignment Clause 7.2 if Clause 7.3 case Clause 7.4 repeat ... while ... do ... Clause 7.5 for Clause 7.6 abort Clause 8. Procedures 8.1 Declarations and Calls 8.2 Forward Declarations 2 9. Aggregates 9.1 Vectors 9.1.1 Creation of Vectors 9.1.2 upb and lwb 9.1.3 Indexing 9.1.4 Equality and Equivalence 9.2 Structures 9.2.1 Creation of Structures 9.2.2 Equality and Equivalence 9.2.3 Indexing 9.3 Images 9.3.1 Creation of Images 9.3.2 Indexing 9.3.3 Depth Selection 9.3.4 Equality and Equivalence 10. Input and Output 10.1 Input 10.2 Output 10.3 i.w, s.w and r.w 10.4 End of File 11. -
Sugarj: Library-Based Syntactic Language Extensibility
SugarJ: Library-based Syntactic Language Extensibility Sebastian Erdweg, Tillmann Rendel, Christian Kastner,¨ and Klaus Ostermann University of Marburg, Germany Abstract import pair.Sugar; Existing approaches to extend a programming language with syntactic sugar often leave a bitter taste, because they cannot public class Test f be used with the same ease as the main extension mechanism private (String, Integer) p = ("12", 34); of the programming language—libraries. Sugar libraries are g a novel approach for syntactically extending a programming Figure 1. Using a sugar library for pairs. language within the language. A sugar library is like an or- dinary library, but can, in addition, export syntactic sugar for using the library. Sugar libraries maintain the compos- 1. Introduction ability and scoping properties of ordinary libraries and are Bridging the gap between domain concepts and the imple- hence particularly well-suited for embedding a multitude of mentation of these concepts in a programming language is domain-specific languages into a host language. They also one of the “holy grails” of software development. Domain- inherit self-applicability from libraries, which means that specific languages (DSLs), such as regular expressions for sugar libraries can provide syntactic extensions for the defi- the domain of text recognition or Java Server Pages for the nition of other sugar libraries. domain of dynamic web pages have often been proposed To demonstrate the expressiveness and applicability of to address this problem [31]. To use DSLs in large soft- sugar libraries, we have developed SugarJ, a language on ware systems that touch multiple domains, developers have top of Java, SDF and Stratego, which supports syntactic to be able to compose multiple domain-specific languages extensibility. -
Development of an Enhanced Automated Software Complexity Measurement System
Journal of Advances in Computational Intelligence Theory Volume 1 Issue 3 Development of an Enhanced Automated Software Complexity Measurement System Sanusi B.A.1, Olabiyisi S.O.2, Afolabi A.O.3, Olowoye, A.O.4 1,2,4Department of Computer Science, 3Department of Cyber Security, Ladoke Akintola University of Technology, Ogbomoso, Nigeria. Corresponding Author E-mail Id:- [email protected] [email protected] [email protected] 4 [email protected] ABSTRACT Code Complexity measures can simply be used to predict critical information about reliability, testability, and maintainability of software systems from the automatic measurement of the source code. The existing automated code complexity measurement is performed using a commercially available code analysis tool called QA-C for the code complexity of C-programming language which runs on Solaris and does not measure the defect-rate of the source code. Therefore, this paper aimed at developing an enhanced automated system that evaluates the code complexity of C-family programming languages and computes the defect rate. The existing code-based complexity metrics: Source Lines of Code metric, McCabe Cyclomatic Complexity metrics and Halstead Complexity Metrics were studied and implemented so as to extend the existing schemes. The developed system was built following the procedure of waterfall model that involves: Gathering requirements, System design, Development coding, Testing, and Maintenance. The developed system was developed in the Visual Studio Integrated Development Environment (2019) using C-Sharp (C#) programming language, .NET framework and MYSQL Server for database design. The performance of the system was tested efficiently using a software testing technique known as Black-box testing to examine the functionality and quality of the system. -
Syntactic Sugar Programming Languages' Constructs - Preliminary Study
ICIT 2011 The 5th International Conference on Information Technology Syntactic Sugar Programming Languages' Constructs - Preliminary Study #1 #2 Mustafa Al-Tamim , Rashid Jayousi Department of Computer Science, Al-Quds University, Jerusalem, Palestine [email protected] 00972-59-9293002 [email protected] 00972-52-7456731 ICIT 2011 The 5th International Conference on Information Technology Syntactic Sugar Programming Languages' Constructs - Preliminary Study #1 #2 Mustafa Al-Tamim , Rashid Jayousi Department of Computer Science, Al-Quds University, Jerusalem, Palestine [email protected] [email protected] Abstract— Software application development is a daily task done syntax and help in making it more readable, easier to write, by developers and code writer all over the world. Valuable less syntax errors and less ambiguous. portion of developers’ time is spent in writing repetitive In this research, we proposed new set of syntactic sugar keywords, debugging code, trying to understand its semantic, constructs that can be composed by a mixture of existing and fixing syntax errors. These tasks become harder when no constructs obtained from some programming languages in integrated development environment (IDE) is available or developers use remote access terminals like UNIX and simple addition to syntactic enhancements suggested by us. Through text editors for code writing. Syntactic sugar constructs in our work as software developer, team leaders, and guiding programming languages are found to offer simple and easy many students in their projects, we noticed that developers syntax constructs to make developers life easier and smother. In write a lot of repetitive keywords in specific parts of code like this paper, we propose a new set of syntactic sugar constructs, packages calling keywords, attributes access modifiers, code and try to find if they really can help developers in eliminating segments and building blocks’ scopes determination symbols syntax errors, make code more readable, more easier to write, and others. -
Compiler Construction E Syntactic Sugar E
Compiler Construction e Syntactic sugar E Compiler Construction Syntactic sugar 1 / 16 Syntactic sugar & Desugaring Syntactic Sugar Additions to a language to make it easier to read or write, but that do not change the expressiveness Desugaring Higher-level features that can be decomposed into language core of essential constructs ) This process is called ”desugaring”. Compiler Construction Syntactic sugar 2 / 16 Pros & Cons for syntactic sugar Pros More readable, More writable Express things more elegantly Cons Adds bloat to the languages Syntactic sugar can affect the formal structure of a language Compiler Construction Syntactic sugar 3 / 16 Syntactic Sugar in Lambda-Calculus The term ”syntactic sugar” was coined by Peter J. Landin in 1964, while describing an ALGOL-like language that was defined in term of lambda-calculus ) goal: replace λ by where Curryfication λxy:e ) λx:(λy:e) Local variables let x = e1 in e2 ) (λx:e2):e1 Compiler Construction Syntactic sugar 4 / 16 List Comprehension in Haskell qs [] = [] qs (x:xs) = qs lt_x ++ [x] ++ qs ge_x where lt_x = [y | y <- xs, y < x] ge_x = [y | y <- xs, x <= y] Compiler Construction Syntactic sugar 5 / 16 List Comprehension in Haskell Sugared [(x,y) | x <- [1 .. 6], y <- [1 .. x], x+y < 10] Desugared filter p (concat (map (\ x -> map (\ y -> (x,y)) [1..x]) [1..6] ) ) where p (x,y) = x+y < 10 Compiler Construction Syntactic sugar 6 / 16 Interferences with error messages ”true” | 42 standard input:1.1-6: type mismatch condition type: string expected type: int function _main() = ( (if "true" then 1 else (42 <> 0)); () ) Compiler Construction Syntactic sugar 7 / 16 Regular Unary - & and | Beware of ( exp ) vs. -
The Commenting Practice of Open Source
The Commenting Practice of Open Source Oliver Arafat Dirk Riehle Siemens AG, Corporate Technology SAP Research, SAP Labs LLC Otto-Hahn-Ring 6, 81739 München 3410 Hillview Ave, Palo Alto, CA 94304, USA [email protected] [email protected] Abstract lem domain is well understood [15]. Agile software devel- opment methods can cope with changing requirements and The development processes of open source software are poorly understood problem domains, but typically require different from traditional closed source development proc- co-location of developers and fail to scale to large project esses. Still, open source software is frequently of high sizes [16]. quality. This raises the question of how and why open A host of successful open source projects in both well source software creates high quality and whether it can and poorly understood problem domains and of small to maintain this quality for ever larger project sizes. In this large sizes suggests that open source can cope both with paper, we look at one particular quality indicator, the den- changing requirements and large project sizes. In this pa- sity of comments in open source software code. We find per we focus on one particular code metric, the comment that successful open source projects follow a consistent density, and assess it across 5,229 active open source pro- practice of documenting their source code, and we find jects, representing about 30% of all active open source that the comment density is independent of team and pro- projects. We show that commenting source code is an on- ject size. going and integrated practice of open source software de- velopment that is consistently found across all these pro- Categories and Subject Descriptors D.2.8 [Metrics]: jects. -
Differences in the Definition and Calculation of the LOC Metric In
Differences in the Definition and Calculation of the LOC Metric in Free Tools∗ István Siket Árpád Beszédes Department of Software Engineering Department of Software Engineering University of Szeged, Hungary University of Szeged, Hungary [email protected] [email protected] John Taylor FrontEndART Software Ltd. Szeged, Hungary [email protected] Abstract The software metric LOC (Lines of Code) is probably one of the most controversial metrics in software engineering practice. It is relatively easy to calculate, understand and use by the different stakeholders for a variety of purposes; LOC is the most frequently applied measure in software estimation, quality assurance and many other fields. Yet, there is a high level of variability in the definition and calculation methods of the metric which makes it difficult to use it as a base for important decisions. Furthermore, there are cases when its usage is highly questionable – such as programmer productivity assessment. In this paper, we investigate how LOC is usually defined and calculated by today’s free LOC calculator tools. We used a set of tools to compute LOC metrics on a variety of open source systems in order to measure the actual differences between results and investigate the possible causes of the deviation. 1 Introduction Lines of Code (LOC) is supposed to be the easiest software metric to understand, compute and in- terpret. The issue with counting code is determining which rules to use for the comparisons to be valid [5]. LOC is generally seen as a measure of system size expressed using the number of lines of its source code as it would appear in a text editor, but the situation is not that simple as we will see shortly. -
PHP 7: What to Expect Lorna Mitchell, PHPUK 2016
PHP 7: What To Expect Lorna Mitchell, PHPUK 2016 Slides available online, help yourself: http://www.lornajane.net/resources Versions of PHP Version Support until Security fixes until PHP 5.5 (expired) 10th July 2016 PHP 5.6 31st December 2016 31st December 2018 PHP 7.0 3rd December 2017 3rd December 2018 see also: http://php.net/supported-versions.php PHP 7 Is Fast PHP 7 Is Fast Why PHP 7 Is Fast • Grew from the phpng project • Influenced by HHVM/Hacklang • Major refactoring of the Zend Engine • More compact data structures throughout • As a result all extensions need updates • http://gophp7.org/gophp7-ext/ Rasmus' stats: http://talks.php.net/fluent15#/6 Abstract Syntax Trees PHP 7 uses an additional AST step during compilation This gives a performance boost and much nicer architecture Abstract Syntax Trees Example code: 1 $a = rand(0,1); 2 3 if($a) { 4 echo "Heads"; 5 } else { 6 echo "Tails"; 7 } Abstract Syntax Trees Tokenized PHP: T_OPEN_TAG: <?php T_VARIABLE: $a T_WHITESPACE: = T_WHITESPACE: T_STRING: rand ( T_LNUMBER: 0 , T_LNUMBER: 1 ) ; Abstract Syntax Trees Abstract syntax tree representation: AST_STMT_LIST AST_ASSIGN AST_VAR a AST_CALL AST_NAME rand AST_ARG_LIST 0 1 New Features Combined Comparison Operator The <=> "spaceship" operator is for quick greater/less than comparison. 1 echo 2 <=> 1; // 1 2 echo 2 <=> 3; // -1 3 echo 2 <=> 2; // 0 Ternary Shorthand Refresher on this PHP 5 feature: 1 echo $count ? $count : 10; // 10 2 echo $count ?: 10; // 10 Null Coalesce Operator Operator ?? is ternary shorthand (?:) but with isset(). 1 $b = 16; 2 3 echo $a ?? 2; // 2 4 echo $a ?? $b ?? 7; // 16 Type Hints PHP 5 has type hinting, allowing you to say what kind of parameter is acceptable in a method call. -
Projectcodemeter Users Manual
ProjectCodeMeter ProjectCodeMeter Pro Software Development Cost Estimation Tool Users Manual Document version 202000501 Home Page: www.ProjectCodeMeter.com ProjectCodeMeter Is a professional software tool for project managers to measure and estimate the Time, Cost, Complexity, Quality and Maintainability of software projects as well as Development Team Productivity by analyzing their source code. By using a modern software sizing algorithm called Weighted Micro Function Points (WMFP) a successor to solid ancestor scientific methods as COCOMO, COSYSMO, Maintainability Index, Cyclomatic Complexity, and Halstead Complexity, It produces more accurate results than traditional software sizing tools, while being faster and simpler to configure. Tip: You can click the icon on the bottom right corner of each area of ProjectCodeMeter to get help specific for that area. General Introduction Quick Getting Started Guide Introduction to ProjectCodeMeter Quick Function Overview Measuring project cost and development time Measuring additional cost and time invested in a project revision Producing a price quote for an Existing project Monitoring an Ongoing project development team productivity Evaluating development team past productivity Evaluating the attractiveness of an outsourcing price quote Predicting a Future project schedule and cost for internal budget planning Predicting a price quote and schedule for a Future project Evaluating the quality of a project source code Software Screen Interface Project Folder Selection Settings File List Charts Summary Reports Extended Information System Requirements Supported File Types Command Line Parameters Frequently Asked Questions ProjectCodeMeter Introduction to the ProjectCodeMeter software ProjectCodeMeter is a professional software tool for project managers to measure and estimate the Time, Cost, Complexity, Quality and Maintainability of software projects as well as Development Team Productivity by analyzing their source code. -
R and C++ Integration
OPENCOURSEWARE ADVANCED PROGRAMMING STATISTICS FOR DATA SCIENCE Ricardo Aler R and C++ integration The purpose of this topic is to show how to improve the efficiency of some R operations by programming them in a compiled language : C++. R is an interpreted language and this makes its execution slow. This is not usually a problem, because R is used as a glue language, in order to put together functions which belong to external libraries. Those libraries have been programmed in Fortran, C, or C++, all of them fast (compiled) languages. Given that R spends most of its time executing those libraries, R inefficiencies do not show in most cases. However, there are some situations, such as executing long loops, for which R is known to be slow, and efficiency might benefit from moving those parts to functions programmed in a compiled language. Package RCpp offers a simple and seamless way of integrating both languages. First it will be explained what is meant by interpreted versus compiled, dynamic versus static typing, and at what situations R is slow (mainly loops and function calling). And therefore, it is worth considering moving those parts to a compiled language like C++. Instead of explaining the complete C++ language, RCpp will be introduced by means of examples. In particular, the following types of functions will be explained, with both the R syntax and the C++ syntax : 1. No inputs, scalar output 2. Scalar input, scalar output 3. Vector input, scalar output 4. Vector input, vector output Finally, the concept of RCpp syntactic sugar will be explained : syntactic sugar is a way of making writing C++ programs more simple, using a simplified yet powerful syntax. -
Counting Software Size: Is It As Easy As Buying a Gallon of Gas?
Counting Software Size: Is It as Easy as Buying A Gallon of Gas? October 22, 2008 NDIA – 11th Annual Systems Engineering Conference Lori Vaughan and Dean Caccavo Northrop Grumman Mission Systems Office of Cost Estimation and Risk Analysis Agenda •Introduction • Standards and Definitions •Sample • Implications •Summary 2 NORTHOP GRUMMAN CORPORTION© Introduction • In what ways is software like gasoline? • In what ways is software not like gasoline? 3 NORTHOP GRUMMAN CORPORTION© Industry Data Suggests… • A greater percentage of the functions of the DoD Weapon Systems are performed by software System Functionality Requiring Software • Increased amount of software in Space Systems and DoD Weapon Systems – Ground, Sea and Space/Missile • Increased amount of software in our daily lives: – Cars, Cell Phones, iPod, Appliances, PDAs… Code Size/Complexity Growth The amount of software used in DoD weapon systems has grown exponentially 4 NORTHOP GRUMMAN CORPORTION© Is There a Standard for Counting Software? • Since, increasing percent of our DoD systems are reliant on software we need to be able to quantify the software size – Historical data collection – Estimation and planning – Tracking and monitoring during program performance • Software effort is proportional to the size of the software being developed – SW Engineering Economics 1981 by Dr. Barry Boehm • “Counting” infers there is a standard • Experience as a prime integrator – Do not see a standard being followed There are software counting standards but the message isn’t out or it is not being followed consistently 5 NORTHOP GRUMMAN CORPORTION© Source Line of Code definition From Wikipedia, the free encyclopedia ”Source lines of code (SLOC) is a software metric used to measure the size of a software program by counting the number of lines in the text of the program's source code. -
Elements of Programming Languages Overview Advanced Constructs
Advanced constructs Functions as objects Iterators and comprehensions Advanced constructs Functions as objects Iterators and comprehensions Overview Elements of Programming Languages We’ve now covered: Lecture 11: Object-oriented functional programming basics of functional programming (with semantics) basics of modular and OO programming (via Scala examples) James Cheney Today, finish discussion of “programming in the large”: University of Edinburgh some more advanced OO constructs and how they co-exist with/support functional October 30, 2017 programming in Scala list comprehensions as an extended example Advanced constructs Functions as objects Iterators and comprehensions Advanced constructs Functions as objects Iterators and comprehensions Advanced constructs Motivating inner class example So far, we’ve covered the “basic” OOP model (circa Java A nested/inner class has access to the private/protected 1.0), plus some Scala-isms members of the containing class Modern languages extend this model in several ways So, we can use nested classes to expose an interface We can define a structure (class/object/trait) inside associated with a specific object: another: As a member of the enclosing class (tied to a specific class List<A> { instance) private A head; or as a static member (shared across all instances) private List<A> tail; As a local definition inside a method class ListIterator<A> implements Iterator<A> { As an anonymous local definition ... (can access head, tail) Java (since 1.5) and Scala support “generics” } } (parameterized types