A slide about Pragmatic Approaches, such as, Evils of Duplication, Orthogonality, Reversibility, Tracer Bullets, Prototypes and Post-it Notes, Domain Languages and Estimating.
Source : A Pragmatic Programmer, written by Andrew Hunt and David Thomas.
This is in continuation with my last presentation about pragmatic programmer. In this I will be discussing the next 10 tips of the book Pramatic Programmer. I hope you will enjoy
The document provides an outline and introduction for a term paper on Agile Software Development. It discusses key aspects of Agile development including the Agile Manifesto, values and principles, methodologies like Extreme Programming (XP) and Scrum, and how Agile development compares to the Waterfall model. The outline covers topics such as the Agile Manifesto, Agile vs Waterfall, methodologies, a case study, performance evaluation, and conclusion.
The document describes the Extreme Programming (XP) model, an agile software development methodology created by Kent Beck. It discusses the key assumptions and practices of XP, including short iterative development cycles, frequent integration and testing, pair programming, and prioritizing customer feedback. The advantages are reducing costs and risks through simplicity, spreading work across the team. Disadvantages include potential lack of upfront design and measurement of quality assurance.
Practicing Data Science: A Collection of Case StudiesKNIMESlides
This is a review of case studies: from easy to complex, from standard to creative. We'll start with a classic churn prediction classification problem and move to another classic - demand prediction. Then we'll determine if and how much easy, standard model training can be automatized. Finally, we'll review a few more creative case studies like AI generated rap songs or neuro-styling of passport images.
First presented by Rosaria Silipo (KNIME) at Strata New York, September 2019.
This PPT throws light on some of the essential elements of the Agile methodology which has become crucial to ensure quality in this day and age. To know more on agile methodology, Scrum Model, Agile Principles and Scrum Board go through this presentation as well as the ones coming soon.
This workshop is designed specially for Queen Mary University of London alumni, in order to teach them TDD.
You will learn: What is TDD, Why and How.
If you want to learn more: https://github.com/MyPitit/TDD
This document provides an overview of agile methodology and compares it to traditional waterfall development. It describes that agile focuses on iterative development with working software delivered frequently in short cycles. The key principles of the agile manifesto are also outlined. Specific agile frameworks like Scrum and Kanban are then explained in more detail. Scrum uses sprints, daily stand-ups, and artifacts like backlogs and burn-down charts. Kanban emphasizes visualizing and limiting work in progress to optimize flow. UX design is noted as an area that can benefit from adopting agile principles.
This is in continuation with my last presentation about pragmatic programmer. In this I will be discussing the next 10 tips of the book Pramatic Programmer. I hope you will enjoy
The document provides an outline and introduction for a term paper on Agile Software Development. It discusses key aspects of Agile development including the Agile Manifesto, values and principles, methodologies like Extreme Programming (XP) and Scrum, and how Agile development compares to the Waterfall model. The outline covers topics such as the Agile Manifesto, Agile vs Waterfall, methodologies, a case study, performance evaluation, and conclusion.
The document describes the Extreme Programming (XP) model, an agile software development methodology created by Kent Beck. It discusses the key assumptions and practices of XP, including short iterative development cycles, frequent integration and testing, pair programming, and prioritizing customer feedback. The advantages are reducing costs and risks through simplicity, spreading work across the team. Disadvantages include potential lack of upfront design and measurement of quality assurance.
Practicing Data Science: A Collection of Case StudiesKNIMESlides
This is a review of case studies: from easy to complex, from standard to creative. We'll start with a classic churn prediction classification problem and move to another classic - demand prediction. Then we'll determine if and how much easy, standard model training can be automatized. Finally, we'll review a few more creative case studies like AI generated rap songs or neuro-styling of passport images.
First presented by Rosaria Silipo (KNIME) at Strata New York, September 2019.
This PPT throws light on some of the essential elements of the Agile methodology which has become crucial to ensure quality in this day and age. To know more on agile methodology, Scrum Model, Agile Principles and Scrum Board go through this presentation as well as the ones coming soon.
This workshop is designed specially for Queen Mary University of London alumni, in order to teach them TDD.
You will learn: What is TDD, Why and How.
If you want to learn more: https://github.com/MyPitit/TDD
This document provides an overview of agile methodology and compares it to traditional waterfall development. It describes that agile focuses on iterative development with working software delivered frequently in short cycles. The key principles of the agile manifesto are also outlined. Specific agile frameworks like Scrum and Kanban are then explained in more detail. Scrum uses sprints, daily stand-ups, and artifacts like backlogs and burn-down charts. Kanban emphasizes visualizing and limiting work in progress to optimize flow. UX design is noted as an area that can benefit from adopting agile principles.
Natural language processing (NLP) analyzes and represents natural language text or speech at linguistic levels to achieve human-like language processing for applications. NLP was influenced by Turing's 1950 paper on machine intelligence and involved early systems like SHRDLU in the 1960s. NLP understands, generates, and integrates natural language through techniques like morphological, syntactic, semantic and discourse analysis to benefit domains like search, translation, sentiment analysis, social media and more.
I normally teach Introduction to Agile and Scrum over a 2 day session to teams. Here is a highly condensed 2-hour version of it that covers agile thinking and introduces scrum as a framework without getting into details.
I use it as a course material for teaching to teams or groups looking to get a perspective on "why" as opposed to "how" aspect of agile.
The document discusses Agile methodology, which is an iterative software development approach based on self-organizing teams. It describes when Agile is useful, such as for complicated projects or when requirements are unclear. Specific Agile methods like Scrum are outlined, including Scrum roles, sprints, and meetings. Advantages include rapid delivery and adaptation, while disadvantages include potential lack of documentation. Tools can help with requirements, planning, tracking, and quality assurance in Agile projects.
This document discusses software testing principles and methodologies. It defines software testing as executing a program under various conditions to check for correctness, completeness, and quality. The document outlines different testing levels from unit to system testing. It also distinguishes between black box and white box testing methods. Finally, it describes different types of system testing like alpha, beta, acceptance, and performance testing.
Regression testing is a continuous testing practice performed to ensure that the software performs the same way, as it did before making any changes. We offer strategic regression testing services to maintain the existing quality of the product, despite the addition of new features to the application.
GPT-2: Language Models are Unsupervised Multitask LearnersYoung Seok Kim
This document summarizes a technical paper about GPT-2, an unsupervised language model created by OpenAI. GPT-2 is a transformer-based model trained on a large corpus of internet text using byte-pair encoding. The paper describes experiments showing GPT-2 can perform various NLP tasks like summarization, translation, and question answering with limited or no supervision, though performance is still below supervised models. It concludes that unsupervised task learning is a promising area for further research.
This document discusses software reliability growth models. It summarizes several key models:
1) The Jelinski-Moranda model assumes random failures, perfect fixes, and all faults contribute equally to failures.
2) The Littlewood models are similar but assume bigger faults are found first.
3) The Goel-Okumoto imperfect debugging model allows for imperfect fixes, where new defects may be introduced when fixing others.
It also briefly discusses other models like the Non-Homogeneous Poisson Process model and Delayed S and Inflection S models.
Good quality code is an essential property of a software because it could lead to financial losses or waste of time needed for further maintenance, modification or adjustments if code quality is not good enough.
Software re-engineering is a process of examining and altering a software system to restructure it and improve maintainability. It involves sub-processes like reverse engineering, redocumentation, and data re-engineering. Software re-engineering is applicable when some subsystems require frequent maintenance and can be a cost-effective way to evolve legacy software systems. The key advantages are reduced risk compared to new development and lower costs than replacing the system entirely.
A Comprehensive Review of Large Language Models for.pptxSaiPragnaKancheti
The document presents a review of large language models (LLMs) for code generation. It discusses different types of LLMs including left-to-right, masked, and encoder-decoder models. Existing models for code generation like Codex, GPT-Neo, GPT-J, and CodeParrot are compared. A new model called PolyCoder with 2.7 billion parameters trained on 12 programming languages is introduced. Evaluation results show PolyCoder performs less well than comparably sized models but outperforms others on C language tasks. In general, performance improves with larger models and longer training, but training solely on code can be sufficient or advantageous for some languages.
This ppt covers the following topics
Software quality
A framework for product metrics
A product metrics taxonomy
Metrics for the analysis model
Metrics for the design model
Metrics for maintenance
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.pdfPo-Chuan Chen
The document describes the RAG (Retrieval-Augmented Generation) model for knowledge-intensive NLP tasks. RAG combines a pre-trained language generator (BART) with a dense passage retriever (DPR) to retrieve and incorporate relevant knowledge from Wikipedia. RAG achieves state-of-the-art results on open-domain question answering, abstractive question answering, and fact verification by leveraging both parametric knowledge from the generator and non-parametric knowledge retrieved from Wikipedia. The retrieved knowledge can also be updated without retraining the model.
The document discusses various black-box testing techniques. It introduces testing, verification, and validation. It then describes black-box and white-box testing. Various types of testing like unit, integration, functional, system, acceptance, regression, and beta testing are explained. Strategies for writing test cases like equivalence partitioning and boundary value analysis are provided. The document emphasizes the importance of planning testing early in the development process.
The document discusses various topics related to software testing including:
1. Software testing helps improve software quality by testing conformance to requirements and is important to uncover errors before delivery to customers.
2. Testing involves specialists at different stages from early development through delivery and includes unit testing of individual components, integration testing of combined components, and system testing of the full system.
3. Proper testing methods include black box testing of inputs/outputs, white box testing of code structures, and testing at different levels from units to full system as well as by independent third parties.
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/TBJqgvXYhfo.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
Sameer's Bio:
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
Understand the different types of requirements and their importance in the Business Analysis process
Learn techniques for gathering and analyzing requirements
Understand how to prioritize requirements based on business value and feasibility
The document describes different software development process models including the waterfall model, prototyping model, incremental development, spiral development, agile methods, and extreme programming. It explains each model and compares their advantages and disadvantages. The waterfall model is most appropriate when requirements are stable while agile methods are best for changing requirements but can be difficult to manage.
This document provides an overview of agile methodology and compares it to traditional waterfall development. It describes waterfall development as a sequential process with distinct phases completed one after another. Agile approaches like Scrum and Kanban are presented as more iterative and adaptive alternatives that focus on delivering working software frequently in short cycles through self-organizing cross-functional teams. Key aspects of Scrum like sprints, daily stand-ups, and product backlogs are defined. Kanban emphasizes visualizing and limiting work in progress to optimize flow. Both aim to incorporate feedback and respond rapidly to changes over rigidly following pre-defined plans.
The document discusses anti-patterns and worst practices in software development. Some examples covered include static cling pattern, flags over objects, premature optimization, copy-paste-compile, and reinventing the wheel. It also shares lessons learned from experiences, such as being mindful of date times across time zones, avoiding building SQL from untrusted inputs, and not being too cute with test data. Overall, the document aims to help developers learn from the mistakes of others and adopt better practices.
The document provides advice for becoming a better programmer based on experience and theory. It discusses maintaining good code quality through principles like DRY (Don't Repeat Yourself), orthogonality, reversibility, prototyping, and domain-specific languages. Specific techniques mentioned include refactoring, testing, modularity, learning new skills regularly, and critically analyzing information to avoid hype. The goal is to produce good-enough software through an iterative process while fighting software entropy.
Natural language processing (NLP) analyzes and represents natural language text or speech at linguistic levels to achieve human-like language processing for applications. NLP was influenced by Turing's 1950 paper on machine intelligence and involved early systems like SHRDLU in the 1960s. NLP understands, generates, and integrates natural language through techniques like morphological, syntactic, semantic and discourse analysis to benefit domains like search, translation, sentiment analysis, social media and more.
I normally teach Introduction to Agile and Scrum over a 2 day session to teams. Here is a highly condensed 2-hour version of it that covers agile thinking and introduces scrum as a framework without getting into details.
I use it as a course material for teaching to teams or groups looking to get a perspective on "why" as opposed to "how" aspect of agile.
The document discusses Agile methodology, which is an iterative software development approach based on self-organizing teams. It describes when Agile is useful, such as for complicated projects or when requirements are unclear. Specific Agile methods like Scrum are outlined, including Scrum roles, sprints, and meetings. Advantages include rapid delivery and adaptation, while disadvantages include potential lack of documentation. Tools can help with requirements, planning, tracking, and quality assurance in Agile projects.
This document discusses software testing principles and methodologies. It defines software testing as executing a program under various conditions to check for correctness, completeness, and quality. The document outlines different testing levels from unit to system testing. It also distinguishes between black box and white box testing methods. Finally, it describes different types of system testing like alpha, beta, acceptance, and performance testing.
Regression testing is a continuous testing practice performed to ensure that the software performs the same way, as it did before making any changes. We offer strategic regression testing services to maintain the existing quality of the product, despite the addition of new features to the application.
GPT-2: Language Models are Unsupervised Multitask LearnersYoung Seok Kim
This document summarizes a technical paper about GPT-2, an unsupervised language model created by OpenAI. GPT-2 is a transformer-based model trained on a large corpus of internet text using byte-pair encoding. The paper describes experiments showing GPT-2 can perform various NLP tasks like summarization, translation, and question answering with limited or no supervision, though performance is still below supervised models. It concludes that unsupervised task learning is a promising area for further research.
This document discusses software reliability growth models. It summarizes several key models:
1) The Jelinski-Moranda model assumes random failures, perfect fixes, and all faults contribute equally to failures.
2) The Littlewood models are similar but assume bigger faults are found first.
3) The Goel-Okumoto imperfect debugging model allows for imperfect fixes, where new defects may be introduced when fixing others.
It also briefly discusses other models like the Non-Homogeneous Poisson Process model and Delayed S and Inflection S models.
Good quality code is an essential property of a software because it could lead to financial losses or waste of time needed for further maintenance, modification or adjustments if code quality is not good enough.
Software re-engineering is a process of examining and altering a software system to restructure it and improve maintainability. It involves sub-processes like reverse engineering, redocumentation, and data re-engineering. Software re-engineering is applicable when some subsystems require frequent maintenance and can be a cost-effective way to evolve legacy software systems. The key advantages are reduced risk compared to new development and lower costs than replacing the system entirely.
A Comprehensive Review of Large Language Models for.pptxSaiPragnaKancheti
The document presents a review of large language models (LLMs) for code generation. It discusses different types of LLMs including left-to-right, masked, and encoder-decoder models. Existing models for code generation like Codex, GPT-Neo, GPT-J, and CodeParrot are compared. A new model called PolyCoder with 2.7 billion parameters trained on 12 programming languages is introduced. Evaluation results show PolyCoder performs less well than comparably sized models but outperforms others on C language tasks. In general, performance improves with larger models and longer training, but training solely on code can be sufficient or advantageous for some languages.
This ppt covers the following topics
Software quality
A framework for product metrics
A product metrics taxonomy
Metrics for the analysis model
Metrics for the design model
Metrics for maintenance
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.pdfPo-Chuan Chen
The document describes the RAG (Retrieval-Augmented Generation) model for knowledge-intensive NLP tasks. RAG combines a pre-trained language generator (BART) with a dense passage retriever (DPR) to retrieve and incorporate relevant knowledge from Wikipedia. RAG achieves state-of-the-art results on open-domain question answering, abstractive question answering, and fact verification by leveraging both parametric knowledge from the generator and non-parametric knowledge retrieved from Wikipedia. The retrieved knowledge can also be updated without retraining the model.
The document discusses various black-box testing techniques. It introduces testing, verification, and validation. It then describes black-box and white-box testing. Various types of testing like unit, integration, functional, system, acceptance, regression, and beta testing are explained. Strategies for writing test cases like equivalence partitioning and boundary value analysis are provided. The document emphasizes the importance of planning testing early in the development process.
The document discusses various topics related to software testing including:
1. Software testing helps improve software quality by testing conformance to requirements and is important to uncover errors before delivery to customers.
2. Testing involves specialists at different stages from early development through delivery and includes unit testing of individual components, integration testing of combined components, and system testing of the full system.
3. Proper testing methods include black box testing of inputs/outputs, white box testing of code structures, and testing at different levels from units to full system as well as by independent third parties.
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/TBJqgvXYhfo.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
Sameer's Bio:
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
Understand the different types of requirements and their importance in the Business Analysis process
Learn techniques for gathering and analyzing requirements
Understand how to prioritize requirements based on business value and feasibility
The document describes different software development process models including the waterfall model, prototyping model, incremental development, spiral development, agile methods, and extreme programming. It explains each model and compares their advantages and disadvantages. The waterfall model is most appropriate when requirements are stable while agile methods are best for changing requirements but can be difficult to manage.
This document provides an overview of agile methodology and compares it to traditional waterfall development. It describes waterfall development as a sequential process with distinct phases completed one after another. Agile approaches like Scrum and Kanban are presented as more iterative and adaptive alternatives that focus on delivering working software frequently in short cycles through self-organizing cross-functional teams. Key aspects of Scrum like sprints, daily stand-ups, and product backlogs are defined. Kanban emphasizes visualizing and limiting work in progress to optimize flow. Both aim to incorporate feedback and respond rapidly to changes over rigidly following pre-defined plans.
The document discusses anti-patterns and worst practices in software development. Some examples covered include static cling pattern, flags over objects, premature optimization, copy-paste-compile, and reinventing the wheel. It also shares lessons learned from experiences, such as being mindful of date times across time zones, avoiding building SQL from untrusted inputs, and not being too cute with test data. Overall, the document aims to help developers learn from the mistakes of others and adopt better practices.
The document provides advice for becoming a better programmer based on experience and theory. It discusses maintaining good code quality through principles like DRY (Don't Repeat Yourself), orthogonality, reversibility, prototyping, and domain-specific languages. Specific techniques mentioned include refactoring, testing, modularity, learning new skills regularly, and critically analyzing information to avoid hype. The goal is to produce good-enough software through an iterative process while fighting software entropy.
This document discusses hexagonal architecture with Symfony. It recommends separating domain logic from infrastructure code like frameworks to increase testability and allow replacing parts independently. It describes defining ports and adapters, with ports representing intentions to communicate and adapters providing implementations. For example, a "buy a ticket" port could be implemented by a HTTP adapter using a controller. The document argues this approach makes code easier to change and maintain while keeping up with framework updates.
Google Interview Prep Guide Software EngineerLewis Lin 🦊
The document provides tips and guidance for preparing for a software engineering interview at Google. It outlines the types of interviews one may expect, including phone interviews focusing on data structures and algorithms and on-site interviews involving coding, algorithms, system design, and more. Candidates are advised to study various programming languages, data structures, algorithms, and computer science topics in-depth to perform well at the technical interviews.
This document discusses refactoring code through careful modifications that do not change functionality. It emphasizes that refactoring requires rigorous unit testing to be done correctly. Successful refactoring can be achieved through uninterrupted work, pair programming, or test-driven development with an automated testing framework. Tests should be written early and often to catch errors introduced during refactoring. The minimum requirement for testing is assert statements, which provide an easy way to start testing without complex tools. Overall, the document stresses that refactoring is best approached as a disciplined process guided by thorough automated testing.
- Many software projects fail to be completed on time and on budget due to unrealistic deadlines, poor estimation of tasks, and changing requirements. Architectural flaws and lack of domain knowledge also contribute to project failures.
- Common problems include inadequate testing, poor code quality, lack of documentation, and developers not wanting to work on code they did not write themselves. Traditional software engineering practices have not changed much over the past 30 years.
- A better approach focuses on rapid feedback through small iterative releases, collaboration with customers, responding flexibly to change, and empowering self-organizing teams. Continuous integration and testing also help catch problems early.
Contemporary Software Engineering Practices Together With EnterpriseKenan Sevindik
The document discusses various software engineering concepts and technologies. It covers topics like prototyping, refactoring, piecemeal growth vs big bang development, agile manifesto principles, design patterns, test driven development, object oriented principles, aspect oriented programming, evolution of enterprise Java technologies like Spring and Hibernate frameworks. It provides recommendations for books related to these topics.
This document discusses applying developer best practices to digital analytics work. It recommends:
1) Using local development with Node.js, build tools, and IDEs to avoid copy-pasting code and work offline.
2) Implementing version control like Git for change history, issue tracking, and teamwork.
3) Writing modular, reusable code through organization conventions and build tools to localize issues.
4) Taking an API-based approach can create a single codebase for multiple properties with continuous integration and auto-generated documentation.
This document discusses information systems analysis and prototyping. It begins with an agenda that covers defining prototyping, the need for it, types of prototypes, prototyping as a methodology, user interface prototyping, and advantages and disadvantages. It then defines prototyping and discusses the need for it to explore problems and solutions with stakeholders. Various types of prototypes are covered, including throwaway, evolutionary, low-fidelity, and high-fidelity. Prototyping is presented as a methodology involving preliminary designs and refinements. The document concludes with risks of prototyping and key learnings around using prototypes to understand requirements and evolve systems.
This document discusses information systems analysis and prototyping. It begins with an agenda that covers defining prototyping, the need for it, types of prototypes, prototyping as a methodology, user interface prototyping, and advantages and disadvantages. It then defines prototyping and discusses the need for it to explore problems and solutions with stakeholders. Various types of prototypes are covered, including throwaway, evolutionary, low-fidelity, and high-fidelity. Prototyping is presented as a methodology involving preliminary designs and refinements. The document concludes with risks of prototyping and key learnings around using prototypes to understand requirements and evolve systems.
This document summarizes the role of an architect and key aspects of architecture. It discusses that an architect understands architectural drivers, designs technical strategies while considering things that are costly to change. An architect fits between the product owner and project manager. The document also covers architecture frameworks, modeling approaches, technical architecture styles, non-functional requirements, and testing non-functional requirements.
- Software engineering is extremely complex and expensive work, with large software systems costing more than buildings and often having high failure rates.
- The two main factors that cause "runaway" software projects that exceed budgets and schedules are poor estimation done too early and unstable requirements that change frequently.
- Programmers are often given impossible tasks with too much work and not enough time, leading them to produce workarounds and quick fixes rather than well-designed solutions.
The document discusses how much attention should be paid to software architecture design. It argues that architecture is important because it acts as the skeleton of the system and influences all its attributes. While not everything requires upfront architectural design, it is important to deliberately address risks. The right amount of architectural effort depends on identifying and prioritizing risks through techniques like risk-driven design. Architectural choices should aim to reduce risks while balancing tradeoffs. It is also important to embed architectural intent into the code to keep models and implementation in sync.
The document discusses several principles and best practices for pragmatic programming. It discusses avoiding duplication by eliminating imposed, inadvertent, impatient, and interdeveloper duplication. It also discusses the principles of orthogonality and reversibility. Orthogonality refers to decoupling unrelated things to increase productivity and reduce risk. Reversibility means designing software in a way that allows for changes in requirements, users, and hardware over time.
The document discusses various aspects of refactoring code including underengineering, overengineering, code smells, Cunningham's metaphor of design debt, when to refactor, how to refactor using small behavior-preserving transformations and test-driven development, the relationship between patterns and refactorings, and important readings on refactoring. The overall message is that refactoring helps improve code quality by removing duplication, simplifying code, and clarifying intent through a process of continuous small changes while preserving behavior.
The document provides information on various DevOps concepts through a question and answer format. It defines design patterns as solutions to common problems faced by developers that represent best practices. It describes continuous deployment as instrumenting important project life cycle steps when moving code to production. It distinguishes between functional testing which targets business goals and requirements, and non-functional testing which focuses on aspects like performance and security. It explains the differences between white box testing which uses internal knowledge and black box testing which does not. It provides examples of resilience test tools like Hystrix and Chaos Monkey. It describes extreme programming as an agile methodology focused on customer satisfaction and team collaboration. It defines pair programming as two programmers working together on the same code. Finally
Formal Versus Agile: Survival of the Fittest? (Paul Boca)AdaCore
The potential for combining agile and formal methods holds promise. Although it might not always be an easy partnership, it will succeed if it can foster a fruitful interchange of expertise between the two communities. In this talk I explain how formal methods can complement agile practices and vice versa. There are no pre-requisites for this talk, except an open mind and a desire to make software development more reliable. Leave any pre-conceptions at home, and be prepared for myths to be dispelled.
hints for computer system design by Butler Lampsonmustafa sarac
This document provides hints for designing successful computer systems based on the author's experience designing and studying various systems. Some key hints discussed are: (1) Keep interfaces and abstractions simple by doing one thing well and avoiding unnecessary generalizations, (2) Separate normal and worst-case functionality to improve performance for common cases, and (3) Make actions atomic and use logging to update state to improve fault tolerance and reliability. The hints are illustrated with examples ranging from hardware to operating systems to applications.
Agile Methodologies And Extreme Programming - Svetlin NakovSvetlin Nakov
1. Agile development and Extreme Programming (XP) are methodologies that focus on iterative development, collaboration, and adaptability.
2. XP consists of 12 key practices including simple design, test-driven development, pair programming, and small releases. It aims to improve quality, reduce risks, and adapt to changing requirements.
3. While XP works well for some projects and teams, its practices may not be suitable or flexible enough for all situations. Developers should understand the principles behind XP and tailor practices as needed for their specific projects.
This document provides an overview of different approaches to project management and software development lifecycles, including waterfall, iterative, spiral, prototyping, and agile models. It discusses the benefits and drawbacks of each approach. The waterfall model is described as the original but inflexible model, while agile approaches emphasize iterative development and customer collaboration over documentation and plans. Project management involves assigning duties to team members over a project's course.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
2. A Pragmatic Approach
Main Topics
Evils of Duplication
Orthogonality
Reversibility
Tracer Bullets
Prototypes and Post-it Notes
Domain Languages
Estimating
3. The Evils of Duplication
DRY – Don’t Repeat Yourself
Every piece of knowledge must have a single,
unambiguous, authoritative representation within
system.
If you change one, you need to change others.
4. The Evils of Duplication
How Does Duplication Rise?
Imposed Duplication
Inadvernt Duplication
Impatient Duplication
Interdeveloper Duplication
Make It Easy to Reuse
5. A Pragmatic Approach
Main Topics
Evils of Duplication
Orthogonality
Reversibility
Tracer Bullets
Prototypes and Post-it Notes
Domain Languages
Estimating
6. Orthogonality
What Is Orthogonality?
It is a critical concept if you want to produce systems
that are easy to design, build, test, and extend.
A term of Geometry.(90 degree lines, x-y axis etc.)
Independent lines.
Parallel movement to an axis doesn’t change your position on
the other one.
In computing, similar to geometry, two orthogonal
things, change in one doesn’t effect the other.
8. Orthogonality
Benefits of Orthogonality
When components of any system are highly interdependent,
there is no such thing as a local fix.
Eliminate Effects Between Unrelated Things
Gain Productivity
Reduce Risk
9. Orthogonality
Comparing DPY with Orthogonality
They are closely related.
In DRY, we are looking to minimize duplication within
system.
With orthogonality, we reduce the interpendency among the
system’s components.
When you combine them, you’ll realize that the systems you
develop are more flexible, more understandable and easier to
debug, test and maintain.
10. A Pragmatic Approach
Main Topics
Evils of Duplication
Orthogonality
Reversibility
Tracer Bullets
Prototypes and Post-it Notes
Domain Languages
Estimating
11. Reversibility
Nothing is more dangerous than an idea if it’s the only
one you have.
Emil-Auguste Chartier, Propos sur la religion, 1938
Nothing stays forever,
if you rely heavily on some fact, you can almost
guarantee that it will change.
Anything you do should be reversible.
12. Reversibility
There is always more than one way to implement
something, and there is usually more than one vendor
available to provide a third-party product.
While you writing your code, product can change.
As time goes by, and your project progresses, you may
find yourself stuck in an untenable position.
With every critical decision, the project team commits
to a smaller target.
The problem is that critical decisions aren’t easily
reversible.
13. Reversibility
Scenario;
Early in the project, to use a relational database from
vendor A.
Later, you discover that the database is slow, but
database from vendor B is faster.
Most of the time, calls to third-party products are
entangled throughout the code
But if you really abstracted the idea of a database out,
you have the flexibility to change horses in midstream.
14. Reversibility
Mistake lies in assuming that any decision is cast in
stone.
Instead of that, think of them more as being written in
the sand at the beach.
A big wave can come along and wipe them out at any
time.
15. Reversibility
Flexible Architecture
Technologies such as CORBA can help insulate portions
of a project from changes in development language or
platform.
Is the performance of Java on that platform not up to
expectations? Recode the client in C++, and nothing else
needs to change.
With a CORBA architecture, you have to take a hit only
for the component you are replacing; the other
components shouldn’t be affected.
16. A Pragmatic Approach
Main Topics
Evils of Duplication
Orthogonality
Reversibility
Tracer Bullets
Prototypes and Post-it Notes
Domain Languages
Estimating
17. Tracer Bullets
Like the gunners, you’re trying to hit a target in the
dark.
Because you may be using algorithms, techniques,
languages, or libraries you aren’t familiar with, you face
a large number of unknowns.
18. Tracer Bullets
Code That Glows in the Dark
We’re looking for something that gets us from a
requirement to some aspect of the final system quickly,
visibly, and repeatably.
Use commands to prove UI could talk to libraries.
19. Tracer Bullets
Advantages of Tracer Code;
Users get to see something working early.
Developers build a structure to work in.
You have an integration platform.
You have something to demonstrate.
You have a better feel for progress.
Tracer Bullets Don’t Always Hit Their Target
Tracer bullets show what you’re hitting. This may not always
be the target. You then adjust your aim until they’re on target.
20. A Pragmatic Approach
Main Topics
Evils of Duplication
Orthogonality
Reversibility
Tracer Bullets
Prototypes and Post-it Notes
Domain Languages
Estimating
21. Prototypes and Post-it Notes
Prototypes are designed to answer just a few questions,
so they are much cheaper and faster to develop than
applications that go into production.
The code can ignore unimportant details.
If you find yourself in an environment where you
cannot give up the details, then you need to ask
yourself if you are really building a prototype at all.
Perhaps a tracer bullet style of development would be
more appropriate in this case.
22. Prototypes and Post-it Notes
Things to Prototype
Architecture
New functionality in an existing system
Structure or contents of external data
Third-party tools or components
Performance issues
User interface design
Prototyping is a learning experience. Its value lies not
in the code produced, but in the lessons learned.
That’s really the point of prototyping.
23. Prototypes and Post-it Notes
When building a prototype, what details can you
ignore?
Correctness
Ex : use dummy data where appropriate.
Completeness
Ex : with only one preselected piece of input data and one
menu item.
Robustness
Ex : check error, crashes are okay
Style
24. A Pragmatic Approach
Main Topics
Evils of Duplication
Orthogonality
Reversibility
Tracer Bullets
Prototypes and Post-it Notes
Domain Languages
Estimating
25. A Pragmatic Approach
Main Topics
Evils of Duplication
Orthogonality
Reversibility
Tracer Bullets
Prototypes and Post-it Notes
Domain Languages
Estimating