Lecture "Software Verification and Validation" in Object Oriented Software Engineering course at Beaconhouse National University Lahore for Spring 2017 Semester by Hafiz Ammar Siddiqui
I have been working on a new breed of estimation methodologies called "Open estimation methodologies". They can be called "Deliverable based estimation methodologies" also. This presentation is about this family of methodologies.
This is one of the estimation methodologies called 'MVC points' that was created to estimate J2EE and .Net applications. I have uploaded a .ppt file for the same also and this is a full paper.
Lecture "Software Verification and Validation" in Object Oriented Software Engineering course at Beaconhouse National University Lahore for Spring 2017 Semester by Hafiz Ammar Siddiqui
I have been working on a new breed of estimation methodologies called "Open estimation methodologies". They can be called "Deliverable based estimation methodologies" also. This presentation is about this family of methodologies.
This is one of the estimation methodologies called 'MVC points' that was created to estimate J2EE and .Net applications. I have uploaded a .ppt file for the same also and this is a full paper.
When the field isn't green: Introducing Model Based Systems Engineering into ...Jeffrey Cohen, P.E.
Many, if not most, Systems Engineering projects are built on top of existing systems. These legacy systems were most likely built with traditional methods. That is, they are document centric with no modeling. Bringing modeling into the new work does not require modeling all of the existing system. This presentation shows how I've brought Model Based Systems Engineering (MBSE) with SysML into organizations working follow-on projects.
Software engineering is defined as a process of analyzing user requirements and then designing, building, and testing software application which will satisfy those requirements. ... It helps you to obtain, economically, software which is reliable and works efficiently on the real machines
ProDebt's Lessons Learned from Planning Technical Debt StrategicallyQAware GmbH
QuASD/PROFES 2017, Innsbruck (Austria): Workshop with Marcus Ciolkowski (Principal IT Consultant at QAware), Liliana Guzmán, Adam Trendowicz, Felix Salfner
Abstract: Due to cost and time constraints, software quality is often neglected in the evolution and adaptation of software. Thus, maintainability suffers, maintenance costs rise, and the development takes longer. These effects are referred to as “technical debt”. The challenge for project managers is to find a balance when using the given budget and schedule, either by reducing technical debt or by adding technical features. This balance is needed to keep time to market for current product releases short and future maintenance costs at an acceptable level.
Method: The project ProDebt aimed at developing an innovative methodology and a software tool to support the strategic planning of technical debt in the context of agile software development. In this project, we created quality models and collected corresponding measurement data for two case studies in two different companies. Alltogether, the two case studies contributed 5-6 years of data, from the end of 2011, resp. mid-2012, until today. Using measurement and effort data, we trained a machine-learning model to predict productivity based on measurement data - representing the technical debt of a file at a given point in time.
Result: We developed a prototype and a prediction model for forecasting potential savings based on proposed refactorings of key drivers of technical debt identified by the model. In this paper, we present the approach and the experiences made during model development.
Enabling Performance Modeling for the Masses: Initial Experiencesabgolla
Performance problems such as sluggish response time or low throughput are especially annoying, frustrating and noticeable to users. Fixing performance problems after they occur results in unplanned expenses and time. Our vision is an MDE-intensive software development paradigm for complex systems in which software designers can evaluate performance early in development, when the analysis can have the greatest impact. We seek to empower designers to do the analysis themselves by automating the creation of performance models out of standard design models. Such performance models can be automatically solved, providing results meaningful to them. In our vision, this automation can be enabled by using model-to-model transformations: First, designers create UML design models embellished with the Modeling and Analysis of Real Time and Embedded systems (MARTE) design specifications; and secondly, such models are transformed to automatically solvable performance models by using QVT. This work reports on our first experiences when implementing these two initial activities.
See full articla at: https://dx.doi.org/10.1007/978-3-030-01042-3_7
This presentation is about a lecture I gave within the "Green Lab" course of the Computer Science master program, of the Vrije Universiteit Amsterdam.
http://www.ivanomalavolta.com
When the field isn't green: Introducing Model Based Systems Engineering into ...Jeffrey Cohen, P.E.
Many, if not most, Systems Engineering projects are built on top of existing systems. These legacy systems were most likely built with traditional methods. That is, they are document centric with no modeling. Bringing modeling into the new work does not require modeling all of the existing system. This presentation shows how I've brought Model Based Systems Engineering (MBSE) with SysML into organizations working follow-on projects.
Software engineering is defined as a process of analyzing user requirements and then designing, building, and testing software application which will satisfy those requirements. ... It helps you to obtain, economically, software which is reliable and works efficiently on the real machines
ProDebt's Lessons Learned from Planning Technical Debt StrategicallyQAware GmbH
QuASD/PROFES 2017, Innsbruck (Austria): Workshop with Marcus Ciolkowski (Principal IT Consultant at QAware), Liliana Guzmán, Adam Trendowicz, Felix Salfner
Abstract: Due to cost and time constraints, software quality is often neglected in the evolution and adaptation of software. Thus, maintainability suffers, maintenance costs rise, and the development takes longer. These effects are referred to as “technical debt”. The challenge for project managers is to find a balance when using the given budget and schedule, either by reducing technical debt or by adding technical features. This balance is needed to keep time to market for current product releases short and future maintenance costs at an acceptable level.
Method: The project ProDebt aimed at developing an innovative methodology and a software tool to support the strategic planning of technical debt in the context of agile software development. In this project, we created quality models and collected corresponding measurement data for two case studies in two different companies. Alltogether, the two case studies contributed 5-6 years of data, from the end of 2011, resp. mid-2012, until today. Using measurement and effort data, we trained a machine-learning model to predict productivity based on measurement data - representing the technical debt of a file at a given point in time.
Result: We developed a prototype and a prediction model for forecasting potential savings based on proposed refactorings of key drivers of technical debt identified by the model. In this paper, we present the approach and the experiences made during model development.
Enabling Performance Modeling for the Masses: Initial Experiencesabgolla
Performance problems such as sluggish response time or low throughput are especially annoying, frustrating and noticeable to users. Fixing performance problems after they occur results in unplanned expenses and time. Our vision is an MDE-intensive software development paradigm for complex systems in which software designers can evaluate performance early in development, when the analysis can have the greatest impact. We seek to empower designers to do the analysis themselves by automating the creation of performance models out of standard design models. Such performance models can be automatically solved, providing results meaningful to them. In our vision, this automation can be enabled by using model-to-model transformations: First, designers create UML design models embellished with the Modeling and Analysis of Real Time and Embedded systems (MARTE) design specifications; and secondly, such models are transformed to automatically solvable performance models by using QVT. This work reports on our first experiences when implementing these two initial activities.
See full articla at: https://dx.doi.org/10.1007/978-3-030-01042-3_7
This presentation is about a lecture I gave within the "Green Lab" course of the Computer Science master program, of the Vrije Universiteit Amsterdam.
http://www.ivanomalavolta.com
QFD (Quality Function Deployment) introduction,
Concept of QFD, History of QFD, Traditional systems & Development of QFD, Technical story as for QFD, Scope of QFD, Benefits of QFD, where does QFD fits, when to use QFD, House of quality (HOQ) model in QFD, relationship matrix of QFD, QFD overview, References, Conclusion
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IT 510 Final Project Guidelines and Rubric Overview The final projec.docxcareyshaunda
IT 510 Final Project Guidelines and Rubric Overview The final project for this course is the creation of a System Proposal Document. In any modern enterprise, it is crucial that all of the different stakeholders, users, inputs, and outputs that relate to the business’s IT systems coalesce in a logical and cohesive way for the systems to be effective. As a member of an IT team, your overarching goal is to ensure that the IT systems ultimately do what the business needs them to do. In this course, you have learned about the key principles and practices underlying the analysis, design, implementation, and management of IT systems. In this final project, you will apply this knowledge by creating a systems proposal document. The project is divided into four milestones, which will be submitted at various points throughout the course to scaffold learning and ensure quality final submissions. These milestones will be submitted in Module Two, Module Four, Module Six, and Module Eight. The final submission will occur in Module Nine. In this assignment, you will demonstrate your mastery of the following course outcomes: Assess the relationship of systems analysis, design, implementation, and development processes as they relate to the management of information technology systems Communicate the paradigms, processes, and activities of systems development to diverse audiences Apply structure and object oriented analysis modeling techniques to analyze, design, and manage information technology systems Construct written and visual representations of the analysis, design, implementation, and management of information technology systems based on the systems development life cycle Prompt You will select your own case study and will apply the content provided, describing the business process to complete the final project. Alternate sources for case studies include the case studies found in the textbook with the exception of the Personal Trainer Case. You can additionally search the internet for business case ideas. You will complete an analysis of an existing information technology system and make recommendations for updates to meet business goals based on your chosen case study. Your final submission will include an introduction, systems requirements, systems design specifications, and an implementation plan. All of the components listed below should be submitted as a single, organized systems proposal document and include screenshots of all relevant diagrams, charts, and tables. I. Introduction: Provide an overview of your selected case. Be sure to provide appropriate citations and reference to the case study you have selected. a) Background: Establish a context for understanding your systems proposal. Specifically, explain any essential paradigms, processes, and activities of the existing information technology systems. b) Problem Statement: What is the problem that needs to be solved? Why is it a problem? What are the impacts to the enterprise? c) A.