Understanding Computer Programs

Introduction to Computer Programs

• A computer program comprises a sequence of instructions.
• These instructions are written in a computer programming language.
• They instruct the computer to perform a specified task.
• Key terminology includes "sequence of instructions" and "computer programming language".
• Computer programming instructs a computer, application, or software program.
• A programming language is a set of instructions used in order to communicate with computers.
• Programming languages serve as a bridge, translating human thoughts into computer instructions.

How Programs Work

• A program instructs a computer to perform tasks by providing a sequence of steps, similar to how humans give directions.
• Program instructions can be called program source code.
• Computer programming can also be called program coding.
• Without programs, computers are inactive. It's actually programs that make computers active.

Classification of Programming Languages (Generations)

• Computer generations evolve with changing technologies, leading to miniaturization, increased speed and memory, and improved reliability.
• Generations are based on evolving chip technology, not specific time frames.
• Five generations are characterized by electrical current flowing through processing mechanisms:

Five Generations of Computers

• Vacuum tubes were the hallmark of the first generation.
• Transistors defined the second generation.
• Integrated circuits marked the third generation.
• Microprocessors characterized the fourth generation.
• Artificial intelligence is the foundation of the fifth generation.

First Generation Languages (1GL)

• Machine-level language is an early form of programming for first-generation computers, operating on a binary level (sequences of 0s and 1s).
• No translators were used.
• Binary programming directly reflects the hardware structure, controlling actions in processing, control, and memory units.
• Binary programming on the flip-flop level was deemed troublesome and impractical for complex problems.

Second Generation Languages (2GL)

• Assembly languages introduce symbolic names and commands, simplifying programming compared to binary.
• Assemblers translate assembly language to machine code.
• Assembly languages reflect the register level hardware structure.
• Instruction set determines an assembly language scope.
• Linking mechanisms enabled separate code assembly, with beginnings of program structuring.

Third Generation Languages (3GL)

• Allow control structures based on logical data objects, offering machine-independent specifications for data, functions, and processes.
• Programmers can focus on problems instead of hardware structure.
• Four programming language groups exist: imperative, declarative, object-oriented, and functional.

Fourth Generation Languages (4GL)

• Deal with databases/query languages and program or application generators.
• Software packages necessitate easy-to-use control mediums for non-specialists.
• Users specify what needs solving instead of how, unlike procedural languages.
• Interactive programming environments are an integral part.

Fifth Generation Languages (5GL)

• Solve problems based on constraints rather than programmer algorithms, enabling computers to "think".
• Artificial Intelligence and Fuzzy Logic become a reality.
• Computers learn problem-solving through constraints.
• Parallel processing and superconductors are used.
• Machines make decisions, which reduces programmer effort, and is easier to learn than 3GL or 4GL.

Computer Capabilities by Generation

• First Generation: Room-sized, slow (hundreds of instructions per second), costly, machine/assembly language, unreliable (failure per second), high power consumption.
• Second Generation: Smaller than first, faster (thousands of instructions per second), less costly, assembly/high-level languages (FORTRAN, COBOL, BASIC), failure per day, low power.
• Third Generation: Smaller than second (mini-computers), faster (millions of instructions per second), less costly, high-level languages (Pascal, C, etc.), failure per week, low power.
• Fourth Generation: Typewriter-sized microcomputers, very fast (tens of millions of instructions per second), low cost, high-level languages (C++, SQL, etc.), failure per month, low power.
• Fifth Generation: Credit card-sized microcomputers, billions of instructions per second, less costly than fourth, AI languages (LISP, PROLOG, etc.), low power.

Computer Hardware Trends by Generation

• First Generation: Vacuum tubes, magnetic drum memory, magnetic tape secondary memory, punched card input/output.
• Second Generation: Transistors, magnetic core memory, magnetic tape/disk secondary memory, punched card input, punched card/print output.
• Third Generation: Integrated circuits, magnetic core primary memory, magnetic tape/disk secondary memory, key-to-tape/disk input, printed/video output.
• Fourth Generation: LSI microprocessors, semiconductor memory (RAM/ROM/cache), magnetic/floppy/optical disk secondary memory, keyboard input, video/audio/print output.
• Fifth Generation: ULSI parallel processing, optical/magnetic disk memory, speech/tactile input, graphic/voice output.

Reasons to Study Programming Language Concepts

• Enhanced ability to express ideas/algorithms.
• Improved background for language choice.
• Better at learning new languages.
• Understanding implementation significance.
• Better use of existing languages.
• Advancement of computing.

Application Domain of Programming Languages

• Prerequisites for developing a programming language, is to have a clear understanding of the application domain.
• Application domain: the section of an organization for which application software is developed.

Scientific Applications

• Characterized by manipulating numbers and arrays, using mathematical and statistical principles as a basis for algorithms.
• Algorithms: statistical significance tests, regression analysis and numerical approximations for the solutions of differential and integral equations.
• Languages: FORTRAN, Pascal, etc.

Data Processing Applications

• Concerned with the creation, maintenance, extraction, and summarization of data in records and files.
• COBOL is commonly used.
• Characterized by facilities for producing elaborate reports, precise ways of describing/storing decimal numbers and characters, and decimal arithmetic operations.

Text Processing Applications

• Involves manipulating natural language text rather than numbers.
• SNOBOL and C language have strong text processing capabilities.

Artificial Intelligence Applications

• Programs designed to emulate intelligent behavior.
• Includes game-playing algorithms, natural language understanding programs, robotics, and expert systems.
• LISP has been the primary AI language, along with PROLOG.
• More Al applications are written in Java, C++ and Python.

Systems Programming Applications

• Developing programs that interface the computer system with the operator.
• Examples include compilers, assemblers, input-output routines, program management and schedules.
• Ada, C, and Modula 2 are examples of programming languages used.

Web Software

• Supported by a multitude of languages.
• Markup languages (HTML) languages, to general-purpose programming languages (Java).
• Dynamic web content requires computation capabilities.
• Markup languages use: XHTML for Markup, JavaScript/PHP for Scripting, and Java/Python for general purposes.

Language Evaluation Criteria

• Expressivity: reflects the ability to show the meaning intended.
• Well-Definiteness: syntax and semantics free of ambiguity.
• Data types and structures support a variety of data values.
• Readability is the ease with which programs can be read and understood.
• Overall Simplicity affects readability.
• Modularity includes language's support for sub-programming and extensibility.

Additional Language Evaluation Criteria

• Input-Output facilities: support for files and database/info retrieval functions.
• Portability: machine independence.
• Efficiency: fast compilation and execution.
• Orthogonality: relatively small set of primitive that can be combined to build the control and data structures.
• Pedagogy: easier to teach and to learn.
• Generality: means that a language is useful in wide range of programming applications

Imperative Programming Languages

• Describe a sequence of commands a machine executes to produce an outcome, focusing on variable and memory.
• Examples:
• C is used for system programming and hardware manipulation due to its low-level capabilities and efficient memory management.
• Fortran supports numerical and array-based operations.
• Cobol is used primarily for applications with an emphasis on readability and maintainability.
• Ada is known for its strong type safety and run-time checks, ensuring code reliability and correctness.
• Pascal enforces structured programming and strong typing.

Object-Oriented Programming Languages

• Use objects, encapsulating data and behavior, to create reusable, modular code.
• Java follows the "write once, run anywhere" philosophy, which develops portable, secure, and robust apps.
• C++ offers classes, inheritance, and polymorphism to achieve efficient memory management and code reuse.
• C#, developed by Microsoft, builds Windows applications and games due to its integration with the .NET framework.
• Python's versatile nature, syntactical simplicity, and extensive libraries make it an attractive choice for various applications.
• Ruby's expressive and readable syntax combined with an object-oriented nature make it a popular choice for web application development.

Functional Programming Languages

• Based on mathematical functions, emphasizing immutable values and the absence of side effects easing parallel processing.
• Haskell: a purely functional language which helps algorithmic, mathematical, and parallel computing.
• Scala: combines object-oriented and functional programming.
• ML: facilitating safe and efficient program execution in language compilers and theorem provers.
• Erlang is functional in nature with lightweight and message-passing concurrency suitable for telecommunication applications and fault-tolerant systems.
• Lisp characterized by its flexible and minimal syntax using artificial intelligence research and symbolic computation.

Logic-Based Programming Languages

• Use declarative statements, expressing relationships and rules between facts, to enable reasoning and problem-solving using artificial intelligence.
  • Prolog is a logic programming language suited for tasks involving symbolic reasoning and manipulation, such as natural language processing.
  • Mercury offers strong typing, mode, and determinism systems creating efficient and reliable code.
  • Logtalk adds modularity, encapsulation, and inheritance, creating reusable and maintainable programs.
  • Datalog is a limited form of Prolog that is focused on deductive database queries and reasoning, and offering efficient evaluation strategies

Picking the Right Programming Language

• Choosing the correct programming language depends on various factors:
• Project goals: Listing the features the language should support determines required capabilities.
• Application type: Selecting a language better suited for web, mobile, or data science applications helps determine target environment and platform.
• Development speed: Enabling rapid development, or allowing for quick prototyping and iteration enables quick tooling.
• Developer experience: The team's proficiency affects the quality of the final product.
• Performance and scalability: Ensure efficient processing time and memory usage.
• Community support: Finding help solve technical challenges quickly can lead to good documentation.
• Maintenance and long-term support: Ensure continued availability of updates, bug fixes, and security patches.

Programming Language Suitability for Different Projects

• Web development: HTML, CSS, and JavaScript are essential for the front end of user website.
• Server-side or back-end development relies on Python (with Django or Flask), Ruby (with Ruby on Rails), PHP (with Laravel or Symfony), and Java (with Spring or JavaServer Faces).
• Mobile app development is achieved with Java or Kotlin for native Android apps, Swift or Objective-C for native iOS apps, or React Native/Flutter for cross-platform apps.
• Data science and machine learning use Python/R/Julia for data analysis, visualization, and machine learning.
• Hardware-level or system-level projects can be achieved with C/C++/Rust with the provision of memory management and system programming.

Programming Language Paradigms

• Programming paradigms guide problem-solving thought, influencing program genres and languages.
• The paradigm adheres to the actual methodology of a programming language.
• Paradigm: a model or world view and is a set of ideas language uses to perform tasks.

Imperative/Procedural Programming Languages

• These are procedures, or blocks of code that perform one task each which are influenced by the von Neumann computer architecture.
• Suitable for small programs with low complexity.
• Consists of assignment statements, data structures and type binding; active procedures manipulate passive data objects.
• Calculator programs can these languages and each of the operations can be developed as separate procedures.

Functional/Applicative Programming Languages

• Syntax is related to the formulation of mathematical functions.
• The solution is broken down into functional units that stitched together to form solution.
• Payroll processing is a good example of the structure where you can maintain employee data, salary, and payments easier.

Logic/Declarative/Ruled Based Programming Languages

• Uses facts and rules to represent information, a logical inference processes the rules to produce results, and the solution is in logical units instead of functional units.
• School management systems can split up functions for teachers, assistants, and coordinators where each user has their own interface and permissions.

Object-Oriented Programming Languages

• Uses data and procedures forming to form a syntactical unit.
• The solution revolves around entities/objects that are part of problem and deals data storage, object behaviors, and interactions for a cohesive solution.
• Payroll management system can deal with the data of employees, their leave structure, etc to build the structure.

Scripting Languages

• A programming language automates the execution of tasks using run time.
• Scripting languages are interpreted at runtime.
• Simplify text processing making them suited to generating HTML pages.

Methodologies:Top-down and Bottom-up

• The top down breaks the problem down into smaller units, and down into even smaller units that allow self-sufficient functionality.
• In bottom-up , components are then interconnected to get higher level components.

Programming Language Trade Offs

• Reliability assesses malfuction detection.
• Expandability increases computer system's requirements.
• Programmability balance programmer simplicity and hardware complexity.
• Compatibility to develop between computer and interfaces, software, etc

Language Implementation

• Compilation, interpretation, hybrid, and just-in-time languages may be used.
• Compilation translates programs into machine language for very fast execution.
• Interpretation has run-time errors than can be displayed.
• Hybrid is a compromise between compilers and pure interpreters for a translated source language.
• Just-in-time compiles subprograms coded the first time that are called and keep the version for subsequent calls.

Syntactic Analysis

• Task to provide a concise description of a programming language .
• New programming languages are subjected to a period of scrutiny by potential users, these are the initial evaluators.
• the structure of these code bases examines the syntax and semantics.

Syntactic Analysis Explained

• Language can be natural(English) or artificial (Java) that have a set of strings of characters from an alphabet, also called statements..
• English has a complex collection of rules to specify it's syntax while Java is syntactically simpler.
• The Syntax' job is to simply check for errors during compiling.

Language Recognizer

• The syntax analysis part of a compiler is a recognizer for the language it translates.
• Syntax analyzer determines whether given programs are syntactically correct(legal sentences).
• Language recognizer acts like a filter, separating legal sentences.

Language Generator

• Generator helps read and understand content.
• If used during compiling the programmer notes if the compiler accepts.
• This requires the programmer to determine if it's correct by comparing it with the generator.

Parsing - Grammar

• In linguistics, parsing describes the process of determining its grammatical structure and formal languages.
• A parser is one of the components that checks for correct syntax using parse trees in computer science.
• Parsing determines how the input can be derived from the start symbol of the grammar:
• Top-down parsing finds leftmost derivations, expanding alternative right-hand-sides of grammar rules.
• Bottom-up parsing locates most basic elements and rewriting them.

Syntactic Ambiguity

• Arises not from the range of meanings of single words, but from the relationship between the words and clauses of a sentence, and the sentence structure implied thereby.

Semantic Analysis

• Verifies the program consists of tokens arranged in a syntactically valid combination, as well as is semantically valid.
• Variables, functions, classes, etc. must be properly declared, access controls, etc. must be respected.
• The compiler needs to know it understands it that is easy to read and used and that every step in that program is viable and correct..
• Syntax-directed semantics maps syntactical to computational model, to ensure proper task acknowledgment.

Operational Semantics

• Meaning in a program is based on necessary calculation steps for execution.

Denotational Semantics

• Focus on the meaning based on elements of a number of abstract mathematical structures.

Axiomatic Semantics

• Is a logic that defines what it will be.

Static Semantics

• Defines structure of valid texts hard to express in syntactic formalisms

Dynamic Semantics

• After specifying data, the machine is instructed to perform operations on the data.

Semantic Analyzer

• Gathers type information.
• The Semantic analyzer is expected to recognize:
  • Type mismatch.
  • Undeclared variable.
  • Reserved identifier misuse.
  • Multiple declaration of variable in a scope.
  • Accessing an out of scope variable.
  • Actual and formal parameter mismatch.

Semantic Analysis Functions

• Type Checking ensures data types are used in a way consistent with their definition.
• Label Checking ensures used in references.
• Flow Control Check that these are used properly and safely.

Fundamental Semantic Issues (Variables, Names, Words)

• In programming, the language uses range from a basic set of values to complex one that represent the given name.
  • The value it holds changes or varies depending on the given condition.

Common Language Differentiations

• Differentiates between integers, non-integers, and characters
• Has Elementary level (such as Integers) which are most common primitives supported in code.
• Also the code can be translated by the compiler with , +, * , /
• Has Structured levels with are structures like arrays, records, and sets as dynamic.

Abstract and Structured Type Levels

• Structured level: Most high-level programming languages allow the definition of structured types which are based on simple types.
• Abstract level: It Programmer defined abstract data types are a set of data objects with declared operations on these data objects.

Naming Rules

• Programming variables and constants commonly take multiple-character names e.g. COST or total.
• Many enforce language such as not starting with a digit (0-9) , or allow _ underscore in a character.
• Some require certain case levels that can lead to internal errors.

Binding Explained

• Binding describes how a variable is created and used (or "bound") by and within the given program, and, possibly, by other programs, as well.
• There are two types of binding; Dynamic, and Static binding.

Dynamic vs Static Binding

• is the process known as Dynamic Dispatch) is the process of mapping a message to a specific sequence of code (method) at runtime.
• Static It occurs first before run time and remains unchanged throughout program execution
• Scope is a lexical aspect of a variable as well.
• Each variables in particular must have their function and scope.

Scopes

• Static :Uses the source code with the given implicit and explicit definitions.
• Dynamic: Each variable has a certain effect, which gives new results with running sub sequential scopes.

Additional Scope Facts

• The scope of a variable can be either local or global.
• Four types of scopes are defined in C language (i) File scope: outside any block or list of parameters, (ii) Block scope: the scope ends with the end of the block. (iii) Function scope: inside a list of parameters where the scope ends. (iv) Function prototype scope: the scope ends with the end of declaration.

Referencing

• The referencing environment - collection of variables that can be used.
• In a static scoped language, one only references variables in the static reference environment.

Syntax vs Semantics

• Syntax refers to code in writing structure.
• Semantics has error related issues during the run time of the coding phase.
• If a code is correct but doesn't give what you will, that's a fault semantic coding issue.

Declarations

• A declaration gives us the name and object details to the programming codes.
• Also defines the binding of the data with and its lifetime.
• The purpose of object declarations is to check that the objects are following a certain parameter in types in all kinds of program execution.

Declaration Details

• Choice of storage representation, and other types declared for objects.
• Types are also polymorphic operations.
• Two types are explicit stating with clear names, and implicit declarations without names assigned.
• Can also be a variable ,scope to define what can show .