Machine Learning in Software EngineeringAlaa Hamouda
Software is nowadays a critical component of our lives and everyday-work working activities. However, as the technological infrastructure of the modern world evolves a great challenge arises for developing high quality software systems with increasing size and complexity. Software engineers and researchers are striving to meet this challenge by developing and implementing software engineering methodologies able to deliver software products of high quality, within budget and time constraints. The field of machine learning in software engineering has recently emerged to provide means for addressing, studying, analyzing, and understanding critical software development issues and at the same time to offer mature machine learning techniques such as artificial neural network, Bayesian networks, decision trees, fuzzy logic, genetic algorithms, and rule induction. Machine learning algorithms have proven to be of great practical value to software engineering. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development tasks could be formulated as learning problems and approached in terms of learning algorithms. In this paper, we first take a look at the characteristics and applicability of some frequently utilized machine learning algorithms. We then present the application of machine learning in the different phases of software engineering that include project planning, requirements analysis, design, implementation, testing and maintenance.
This calculator has been developed by me. It gives high precision results which
Normal calculator can not give. It is helpful in calculations for Space technology,
Supercomputers, Nano technology etc. I can give this calculator to interested people.
The customer will typically be required to provide or choose a billing address, a mailing address, a delivery option, and payment details like a credit card number. As soon as the order is placed, a customer notification email is delivered.
Machine Learning in Software EngineeringAlaa Hamouda
Software is nowadays a critical component of our lives and everyday-work working activities. However, as the technological infrastructure of the modern world evolves a great challenge arises for developing high quality software systems with increasing size and complexity. Software engineers and researchers are striving to meet this challenge by developing and implementing software engineering methodologies able to deliver software products of high quality, within budget and time constraints. The field of machine learning in software engineering has recently emerged to provide means for addressing, studying, analyzing, and understanding critical software development issues and at the same time to offer mature machine learning techniques such as artificial neural network, Bayesian networks, decision trees, fuzzy logic, genetic algorithms, and rule induction. Machine learning algorithms have proven to be of great practical value to software engineering. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development tasks could be formulated as learning problems and approached in terms of learning algorithms. In this paper, we first take a look at the characteristics and applicability of some frequently utilized machine learning algorithms. We then present the application of machine learning in the different phases of software engineering that include project planning, requirements analysis, design, implementation, testing and maintenance.
This calculator has been developed by me. It gives high precision results which
Normal calculator can not give. It is helpful in calculations for Space technology,
Supercomputers, Nano technology etc. I can give this calculator to interested people.
The customer will typically be required to provide or choose a billing address, a mailing address, a delivery option, and payment details like a credit card number. As soon as the order is placed, a customer notification email is delivered.
Nowadays software systems are essential to the environment of most organizations, and their maintenance is a key point to support business dynamics. Thus, reverse engineering legacy systems for knowledge reuse has become a major concern in software industry. This article, based on a survey about reverse engineering tools, discusses a set of functional and nonfunctional requirements for an effective tool for reverse engineering, and observes that current tools only partly support these requirements. In addition, we define new requirements, based on our group’s experience and industry feedback, and present the architecture and implementation of LIFT: a Legacy InFormation retrieval Tool, developed based on these demands. Furthermore, we discuss the compliance of LIFT with the defined requirements. Finally, we applied the LIFT in a reverse engineering project of a 210KLOC NATURAL/ADABAS system of a financial institution and analyzed its effectiveness and scalability, comparing data with previous similar projects performed by the same institution.
Function Point Analysis & Cocomo. Two main estimation methods for structured and object oriented methodology estimations. Cocomo is widely used in estimating where Rational Unified Process is followed.
Esoft Metro Campus - Diploma in Information Technology - (Module VII) Software Engineering
(Template - Virtusa Corporate)
Contents:
What is software?
Software classification
Attributes of Software
What is Software Engineering?
Software Process Model
Waterfall Model
Prototype Model
Throw away prototype model
Evolutionary prototype model
Rapid application development
Programming styles
Unstructured programming
Structured programming
Object oriented programming
Flow charts
Questions
Pseudo codes
Object oriented programming
OOP Concepts
Inheritance
Polymorphism
Encapsulation
Generalization/specialization
Unified Modeling Language
Class Diagrams
Use case diagrams
Software testing
Black box testing
White box testing
Software documentation
Nowadays software systems are essential to the environment of most organizations, and their maintenance is a key point to support business dynamics. Thus, reverse engineering legacy systems for knowledge reuse has become a major concern in software industry. This article, based on a survey about reverse engineering tools, discusses a set of functional and nonfunctional requirements for an effective tool for reverse engineering, and observes that current tools only partly support these requirements. In addition, we define new requirements, based on our group’s experience and industry feedback, and present the architecture and implementation of LIFT: a Legacy InFormation retrieval Tool, developed based on these demands. Furthermore, we discuss the compliance of LIFT with the defined requirements. Finally, we applied the LIFT in a reverse engineering project of a 210KLOC NATURAL/ADABAS system of a financial institution and analyzed its effectiveness and scalability, comparing data with previous similar projects performed by the same institution.
Function Point Analysis & Cocomo. Two main estimation methods for structured and object oriented methodology estimations. Cocomo is widely used in estimating where Rational Unified Process is followed.
Esoft Metro Campus - Diploma in Information Technology - (Module VII) Software Engineering
(Template - Virtusa Corporate)
Contents:
What is software?
Software classification
Attributes of Software
What is Software Engineering?
Software Process Model
Waterfall Model
Prototype Model
Throw away prototype model
Evolutionary prototype model
Rapid application development
Programming styles
Unstructured programming
Structured programming
Object oriented programming
Flow charts
Questions
Pseudo codes
Object oriented programming
OOP Concepts
Inheritance
Polymorphism
Encapsulation
Generalization/specialization
Unified Modeling Language
Class Diagrams
Use case diagrams
Software testing
Black box testing
White box testing
Software documentation
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. SEG4110 - Topic S - Program Analysis 2
Copyright Note
These slides are derived from work by
Bil Tzerpos a faculty member at York University
3. SEG4110 - Topic S - Program Analysis 3
Program Analysis
Extracting information, in order to present abstractions
of, or answer questions about, a software system
Static Analysis
• Examines the source code
Dynamic Analysis
• Examines the system as it is executing
4. SEG4110 - Topic S - Program Analysis 4
What are we looking for when performing
program analysis?
Depends on our goals and the system
• In almost any language, we can find out information about variable
usage
• In an OO environment, we can find out which classes use other
classes, what is the inheritance structure, etc.
• We can also find potential blocks of code that can never be executed
in running the program (dead code)
• Typically, the information extracted is in terms of entities and
relationships
—Can be metamodelled in a class diagram
5. SEG4110 - Topic S - Program Analysis 5
Entities
Entities are individuals that live in the system, and
attributes associated with them.
Some examples:
• Classes, along with information about their superclass,
their scope, and ‘where’ in the code they exist.
• Methods/functions and what their return type or
parameter list is, etc.
• Variables and what their types are, and whether or not
they are static, etc.
6. SEG4110 - Topic S - Program Analysis 6
Relationships
Relationships are interactions between the entities in the
system
Relationships include
• Classes inheriting from one another.
• Methods in one class calling the methods of another
class, and methods within the same class calling one
another.
• A method referencing an attribute.
7. SEG4110 - Topic S - Program Analysis 7
Information format for data extracted during
program analysis
Many different formats in use
• Simple but effective: RSF (Rigi Standard Format)
inherit TRIANGLE SHAPE
• TA (Tuple Attribute) is an extension of RSF that includes
a schema
$INSTANCE SHAPE Class
• GXL is a XML-based extension of TA
—Blow-up factor of 10 or more makes it rather
cumbersome
• New formats based on YAML being developed
8. SEG4110 - Topic S - Program Analysis 8
Representation of extracted information
A fundamental issue in re-engineering
• Provides
—means to generate abstractions
—input to a computational model for analyzing and
reasoning about programs
—means for translation and normalization of programs
9. SEG4110 - Topic S - Program Analysis 9
Key questions regarding representations of
extracted information
What are the strengths and weaknesses of each
representations of programs?
What levels of abstraction are useful?
10. SEG4110 - Topic S - Program Analysis 10
Abstract Syntax Trees
A translation of the source text in terms of operands and
operators
Omits superficial details, such as comments, whitespace
All necessary information to generate further
abstractions is maintained
11. SEG4110 - Topic S - Program Analysis 11
AST production
Four necessary elements to produce an AST:
• Lexical analyzer (turn input strings into tokens)
• Grammar (turn tokens into a parse tree)
• Domain Model (defines the nodes and arcs allowable in
the AST)
• Linker (annotates the AST with global information, e.g.
data types, scoping etc.)
12. SEG4110 - Topic S - Program Analysis 12
AST example
Input string: 1 + /* two */ 2
Parse Tree:
AST (without
global info) 2
1
+
int
int
Add
1 2
arg1 arg2
13. SEG4110 - Topic S - Program Analysis 13
Static Analysis
Involves parsing the source code
Usually creates an Abstract Syntax Tree
Borrows heavily from compiler technology
• but stops before code generation
Requires a grammar for the programming language
Can be very difficult to get right
14. SEG4110 - Topic S - Program Analysis 14
CppETS
CppETS is a benchmark for C++ extractors
A collection of C++ programs that pose various
problems commonly found in parsing and reverse
engineering
Static analysis research tools typically get about 60%
of the problems right
15. SEG4110 - Topic S - Program Analysis 15
Example program
#include <iostream.h>
class Hello {
public: Hello(); ~Hello();
};
Hello::Hello()
{ cout << "Hello, world.n"; }
Hello::~Hello()
{ cout << "Goodbye, cruel world.n"; }
main() {
Hello h;
return 0;
}
16. SEG4110 - Topic S - Program Analysis 16
Example Q&A
How many member methods are in the Hello
class?
Where are these member methods used?
Answer: Two, the constructor (Hello::Hello()) and
destructor (Hello::~Hello())
Answer: The constructor is called implicitly when
an instance of the class is created. The destructor
is called implicitly when the execution leaves the
scope of the instance.
17. SEG4110 - Topic S - Program Analysis 17
Static analysis in IDEs
High-level languages lend themselves better to static analysis
needs
• Rational Software Modeler does this with UML and Java
Unfortunately, most legacy systems are not written in either of
these languages
18. SEG4110 - Topic S - Program Analysis 18
Static analysis pipeline
Source code Parser Abstract Syntax Tree
Fact base
Fact extractor
Applications
Metrics tool
Visualizer
19. SEG4110 - Topic S - Program Analysis 19
Dynamic Analysis
Provides information about the run-time behaviour of
software systems, e.g.
• Component interactions
• Event traces
• Concurrent behaviour
• Code coverage
• Memory management
Can be done with a debugger
20. SEG4110 - Topic S - Program Analysis 20
Instrumentation
Augments the subject program with code that
• transmits events to a monitoring application
• or writes relevant information to an output file
A profiler tool can be used to examine the output file and
extract relevant facts from it
Instrumentation affects the execution speed and storage
space requirements of the system
21. SEG4110 - Topic S - Program Analysis 21
Instrumentation process
Source code Annotator Annotated program
Instrumented
executable
Compiler
Annotation
script
22. SEG4110 - Topic S - Program Analysis 22
Dynamic analysis pipeline
Instrumented
executable
CPU Dynamic analysis data
Fact base
Profiler
Applications
Metrics tool
Visualizer
23. SEG4110 - Topic S - Program Analysis 23
Non-instrumented approach
One can also use debugger log files to obtain dynamic information
• Disadvantage: Limited amount of information provided
• Advantages: Less intrusive, more accurate performance
measurements
24. SEG4110 - Topic S - Program Analysis 24
Dynamic analysis issues
Ensuring good code coverage is a key concern
A comprehensive test suite is required to ensure that all paths in the
code will be exercised
Results may not generalize to future executions
The size of run-time information is extraordinary large
25. SEG4110 - Topic S - Program Analysis 25
Summary: Static vs. Dynamic Analysis
Static Analysis
Reasons over all possible
behaviours (general results)
Conservative and sound
Challenge: Choose good
abstractions
Dynamic Analysis
Observes a small number of
behaviours (specific results)
Precise and fast
Challenge: Select representative
test cases
26. SEG4110 - Topic S - Program Analysis 26
Program Transformation
The act of changing one program into another
• from a source language to a target language
This is possible because of a program’s well-defined structure
• But for validity, we have to be aware of the semantics of each
structure
Used in many areas of software engineering:
• Compiler construction
• Software visualization
• Documentation generation
• Automatic software renovation
27. SEG4110 - Topic S - Program Analysis 27
Program transformation application
examples
Converting to a new language dialect
Migrating from a procedural language to an object-oriented one,
e.g. C to C++
Adding code comments
Requirement upgrading
• e.g. using 4 digits for years instead of 2 (Y2K)
Structural improvements
• e.g. changing GOTOs to control structures
Pretty printing
28. SEG4110 - Topic S - Program Analysis 28
Simple program transformation
Modify all arithmetic expressions to reduce the number
of parentheses using the formula: (a+b)*c = a*c + b*c
x := (2+5)*3
becomes
x := 2*3 + 5*3
29. SEG4110 - Topic S - Program Analysis 29
Two types of transformations
Translation
• Source and target language are different
• Semantics remain the same
Rephrasing
• Source and target language are the same
• Goal is to improve some aspect of the program such as
its understandability or performance
• Semantics might change
30. SEG4110 - Topic S - Program Analysis 30
Transformation tools
There are many transformation tools
Program-Transformation.org lists 90 of them
• http://www.program-transformation.org/
• TXL is one of the best
Most are based on ‘term rewriting’
• Other solutions use functional programming, lambda
calculus, etc.