Effects Based Planning And Assessment
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1. The document discusses using hierarchical modeling techniques to help with effects-based planning and assessment. It proposes an 8-phase method called Risk Filtering, Ranking, and Management to reduce the number of options and develop courses of action. 2. The method first identifies and filters options based on available resources. It then filters further based on phase and scope, and quantitatively evaluates and ranks remaining options. It also considers risks and impacts to forces and future options. 3. The approach aims to reduce dimensionality challenges in effects-based operations planning by qualitatively and quantitatively screening many potential options and actions down to a manageable set of courses of action.
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![Using Hierarchical Modeling to Assist Effects Based Planning and Assessment Lt Col Dave “Doc” Denhard, PhD Maj Robert “Dagwood” Umstead Air Force Institute of Technology Wright-Patterson, Air Force Base Ohio United States The views expressed in this presentation are those of the authors and do not reflect the official policy or position of the Air Force, Department of Defense or the U.S. Government. Contact info: [email_address] 01-937-255-3355 X3325](https://image.slidesharecdn.com/effects-based-planning-and-assessment-1227303986488547-9/85/Effects-Based-Planning-And-Assessment-1-320.jpg)
![Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Machine Learning approaches are good in solving problems that have less information. In most cases, the
software domain problems characterize as a process of learning that depend on the various circumstances
and changes accordingly. A predictive model is constructed by using machine learning approaches and
classified them into defective and non-defective modules. Machine learning techniques help developers to
retrieve useful information after the classification and enable them to analyse data from different
perspectives. Machine learning techniques are proven to be useful in terms of software bug prediction. This
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of different machine learning techniques for software bug prediction. Results showed most of the machine
learning methods performed well on software bug datasets.
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Big shadow test
Big shadow test
Big-Shadow-Test Method is used to solve a large simultaneous problem as a sequence of smaller simultaneous problems.
Shadow tests are no regular tests; their items are always returned to the pool. They are only assembled to balance the selection of items between current and future tests. Because of their presence, they neutralize the greedy character inherent in sequential test-assembly methods. In doing so, they prevent the best items from being assigned only to earlier tests and keep the later test-assembly problems feasible.
On the application of SAT solvers for Search Based Software Testing
The document discusses using SAT solvers to solve optimization problems in search-based software testing. It introduces optimization problems and techniques like metaheuristics and evolutionary algorithms. The document then focuses on applying SAT solvers to the test suite minimization problem, which aims to minimize the number of tests needed to achieve full code coverage. It describes translating the optimization problem into a SAT instance that can be solved by SAT solvers to obtain optimal solutions.
Software Analytics In Action: A Hands-on Tutorial on Mining, Analyzing, Model...
Software Analytics In Action: A Hands-on Tutorial on Mining, Analyzing, Model...Chakkrit (Kla) Tantithamthavorn
Software analytics focuses on analyzing and modeling a rich source of software data using well-established data analytics techniques in order to glean actionable insights for improving development practices, productivity, and software quality. However, if care is not taken when analyzing and modeling software data, the predictions and insights that are derived from analytical models may be inaccurate and unreliable. The goal of this hands-on tutorial is to guide participants on how to (1) analyze software data using statistical techniques like correlation analysis, hypothesis testing, effect size analysis, and multiple comparisons, (2) develop accurate, reliable, and reproducible analytical models, (3) interpret the models to uncover relationships and insights, and (4) discuss pitfalls associated with analytical techniques including hands-on examples with real software data. R will be the primary programming language. Code samples will be available in a public GitHub repository. Participants will do exercises via either RStudio or Jupyter Notebook through Binder.Ijcatr04051006
One of the core quality assurance feature which combines fault prevention and fault detection, is often known as testability approach also. There are many assessment techniques and quantification method evolved for software testability prediction which actually identifies testability weakness or factors to further help reduce test effort. This paper examines all those measurement techniques that are being proposed for software testability assessment at various phases of object oriented software development life cycle. The aim is to find the best metrics suit for software quality improvisation through software testability support. The ultimate objective is to establish the ground work for finding ways reduce the testing effort by improvising software testability and its assessment using well planned guidelines for object-oriented software development with the help of suitable metrics.
ONE HIDDEN LAYER ANFIS MODEL FOR OOS DEVELOPMENT EFFORT ESTIMATION
ONE HIDDEN LAYER ANFIS MODEL FOR OOS DEVELOPMENT EFFORT ESTIMATIONInternational Journal of Technical Research & Application
In the present paper, applicability and
capability of A.I techniques for effort estimation prediction has
been investigated. It is seen that neuro fuzzy models are very
robust, characterized by fast computation, capable of handling
the distorted data. Due to the presence of data non-linearity, it is
an efficient quantitative tool to predict effort estimation. The one
hidden layer network has been developed named as OHLANFIS
using MATLAB simulation environment.
Here the initial parameters of the OHLANFIS are
identified using the subtractive clustering method. Parameters of
the Gaussian membership function are optimally determined
using the hybrid learning algorithm. From the analysis it is seen
that the Effort Estimation prediction model developed using
OHLANFIS technique has been able to perform well over normal
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Towards a Better Understanding of the Impact of Experimental Components on De...Chakkrit (Kla) Tantithamthavorn
Software Quality Assurance (SQA) teams play a critical role in the software development process to ensure the absence of software defects. It is not feasible to perform exhaustive SQA tasks (i.e., software testing and code review) on a large software product given the limited SQA resources that are available. Thus, the prioritization of SQA efforts is an essential step in all SQA efforts. Defect prediction models are used to prioritize risky software modules and understand the impact of software metrics on the defect-proneness of software modules. The predictions and insights that are derived from defect prediction models can help software teams allocate their limited SQA resources to the modules that are most likely to be defective and avoid common past pitfalls that are associated with the defective modules of the past. However, the predictions and insights that are derived from defect prediction models may be inaccurate and unreliable if practitioners do not control for the impact of experimental components (e.g., datasets, metrics, and classifiers) on defect prediction models, which could lead to erroneous decision-making in practice. In this thesis, we investigate the impact of experimental components on the performance and interpretation of defect prediction models. More specifically, we investigate the impact of the three often overlooked experimental components (i.e., issue report mislabelling, parameter optimization of classification techniques, and model validation techniques) have on defect prediction models. Through case studies of systems that span both proprietary and open-source domains, we demonstrate that (1) issue report mislabelling does not impact the precision of defect prediction models, suggesting that researchers can rely on the predictions of defect prediction models that were trained using noisy defect datasets; (2) automated parameter optimization for classification techniques substantially improve the performance and stability of defect prediction models, as well as they change their interpretation, suggesting that researchers should no longer shy from applying parameter optimization to their models; and (3) the out-of-sample bootstrap validation technique produces a good balance between bias and variance of performance estimates, suggesting that the single holdout and cross-validation families that are commonly-used nowadays should be avoided.Ghotra icse
This document discusses a study on the impact of classification techniques on the performance of defect prediction models. The study finds that unlike prior work that grouped techniques into 2 similar ranks, this study using non-overlapping statistical ranks, expanded scope, and clean data sources grouped techniques into 4 distinct ranks, showing top techniques like logistic model trees and logistic regression outperform others. This suggests classification technique selection matters more than previously thought. The study concludes that experimenting with different available techniques could improve defect prediction model performance.
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The document discusses project risk management and risk-informed decision making. It describes identifying alternatives, analyzing risks of alternatives, and selecting alternatives based on risk analysis. Key steps include identifying objectives and constraints, compiling alternatives, choosing analysis methods, conducting risk assessments, developing risk-normalized performance commitments, deliberating alternatives, and selecting an alternative.
Research proposal
This document is a research proposal on attribute selection and representation for software defect prediction. The proposal discusses limitations in existing attribute selection methods and the importance of pre-processing data. It aims to propose a new attribute selection method that improves accuracy by addressing shortcomings, and to study appropriate classifiers. The methodology involves a literature review on pre-processing, attribute selection and classification methods. It will then propose and implement a new attribute selection process, compare it using different classifiers and pre-processing, and evaluate it against existing techniques in a technical report.
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Leadership at all levels of a healthcare organization plays a central role in patient safety, yet few evidence-based interventions exist to meet this critical function. Simulation and gaming have demonstrated improvement in technical and non-technical competencies of healthcare workers, as well as organizational learning and continuous improvement but has not been broadly applied to the patient safety.
Our research developed, implemented and evaluated a gaming application for building safety related leadership competencies along with strategy development for executive and mid-level healthcare leaders.
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Kitamura1992
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Performance analysis of the
This document summarizes recent approaches in opinion mining and sentiment analysis (OMSA) in online social networks. It discusses 15 different frameworks and methods that have been proposed, including the Twitter Opinion Mining framework, an election prediction model using user influence factors, and a fuzzy deep belief network approach. The document analyzes the key steps and characteristics of each approach, such as data collection, preprocessing, classification techniques, and performance results. Overall, the paper reviews the state-of-the-art in OMSA research and highlights areas for future improvements.
Evaluation of eLearning
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Application of the analytic hierarchy process (AHP) for selection of forecast...
In this paper, we described an application of the Analytic Hierarchy Process (AHP) for the ranking and selection of forecasting software. AHP is a multi-criteria decision making (MCDM) approach, which is based on the pair-wise comparison
of elements of a given set with respect to multiple criteria. Even though there are applications of the AHP to software selection problems, we have not encountered a study that involves forecasting software. We started our analysis by filtering
among forecasting software that were found on the Internet by undergraduate students as a part of a course project. Then we processed a second filtering step, where we reduced the number of software to be examined even further. Finally we
constructed the comparison matrices based upon the evaluations of three “semiexperts”, and obtained a ranking of forecasting software of the selected software using the Expert Choice software. We report our findings and our insights, together with the results of a sensitivity analysis.
http://research.sabanciuniv.edu.
STATE-OF-THE-ART IN EMPIRICAL VALIDATION OF SOFTWARE METRICS FOR FAULT PRONEN...
With the sharp rise in software dependability and failure cost, high quality has been in great demand.However, guaranteeing high quality in software systems which have grown in size and complexity coupled with the constraints imposed on their development has become increasingly difficult, time and resource consuming activity. Consequently, it becomes inevitable to deliver software that have no serious faults. In
this case, object-oriented (OO) products being the de facto standard of software development with their unique features could have some faults that are hard to find or pinpoint the impacts of changes. The earlier faults are identified, found and fixed, the lesser the costs and the higher the quality. To assess product quality, software metrics are used. Many OO metrics have been proposed and developed. Furthermore,
many empirical studies have validated metrics and class fault proneness (FP) relationship. The challenge is which metrics are related to class FP and what activities are performed. Therefore, this study bring together the state-of-the-art in fault prediction of FP that utilizes CK and size metrics. We conducted a systematic literature review over relevant published empirical validation articles. The results obtained are
analysed and presented. It indicates that 29 relevant empirical studies exist and measures such as complexity, coupling and size were found to be strongly related to FP.
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This document analyzes and compares maintainability metrics for aspect-oriented software (AOS) and object-oriented software (OOS) using five projects. It discusses metrics like number of children, depth of inheritance tree, lack of cohesion of methods, weighted methods per class, and lines of code. The results show that for most metrics like NOC, DIT, LCOM, and WMC, the mean values are higher for OOS compared to AOS, indicating that AOS is generally more maintainable based on these metrics. LOC is also lower on average for AOS. The study concludes that an AOP version is more maintainable than an OOP version according to the chosen metrics.
Comparative performance analysis
Machine learning techniques can be used to analyse data from different perspectives and enable developers to retrieve useful information. Machine learning techniques are proven to be useful
in terms of software bug prediction. In this paper, a comparative performance analysis of
different machine learning techniques is explored for software bug prediction on public
available data sets. Results showed most of the machine learning methods performed well on
software bug datasets.
Ranking The Refactoring Techniques Based on The External Quality Attributes
The selection of appropriate decisions is a significant issue that might lead to more satisfactory results. The difficulty comes when there are several alternatives and when all of them have the same chance of being selected. It is important, therefore, to find the kinds of priorities among all of these alternatives in order to choose the most appropriate one. The analytic hierarchy process (AHP) is capable of structuring decision problems and finding mathematically determined judgments built on knowledge and experience. This suggests that AHP should prove useful in agile software development where complex decisions occur routinely. This paper presents an example of using the AHP to rank the refactoring techniques based on the external code quality attributes. XP encourages applying the refactoring where the code smells bad. However, refactoring may consume more time and efforts. Therefore, to maximize the benefits of the refactoring in less time and effort, AHP has been applied to achieve this purpose. It was found that ranking the refactoring techniques helped the XP team to focus on the technique that improve the code and the XP development process in general. :
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This document provides a comprehensive overview and categorization of feature selection methods for machine learning classification problems. It identifies the four main steps in typical feature selection methods: generation procedure, evaluation function, stopping criterion, and validation procedure. Generation procedures are categorized as complete, heuristic, or random. Evaluation functions are categorized as distance measures, information measures, dependence measures, consistency measures, or classifier error rates. The document then surveys 32 existing feature selection methods and categorizes them based on the type of generation procedure and evaluation function used. It provides representative examples and discusses strengths and weaknesses of different approaches.
Configuration Navigation Analysis Model for Regression Test Case Prioritization
Regression testing has been receiving increasing attention nowadays. Numerous regression testing strategies have been proposed. Most of them take into account various metrics like cost as well as the ability to find faults quickly thereby saving overall testing time. In this paper, a new model called the Configuration Navigation Analysis Model is proposed which tries to consider all stakeholders and various testing aspects while prioritizing regression test cases.
Software metrics
Software metricsIntroduction
Attributes of Software Metrics
Activities of a Measurement Process
Types
Normalization of Metrics
Help software engineers to gain insight into the design and construction of the software
Activities of a Measurement Process
To answer this we need to know the size & complexity of the projects.
But if we normalize the measures, it is possible to compare the two
For normalization we have 2 ways-
Size-Oriented Metrics
Function Oriented Metrics
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Games to Improve Clinical Practice and Healthcare Administration
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accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
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- 1. Using Hierarchical Modeling to Assist Effects Based Planning and Assessment Lt Col Dave “Doc” Denhard, PhD Maj Robert “Dagwood” Umstead Air Force Institute of Technology Wright-Patterson, Air Force Base Ohio United States The views expressed in this presentation are those of the authors and do not reflect the official policy or position of the Air Force, Department of Defense or the U.S. Government. Contact info: [email_address] 01-937-255-3355 X3325
- 3. Effects-Based Operations Enemy Systems of Systems Model Effect 1 Action (via Resource) [Node A] [Node B] [Node C] Friendly Course of Action (COA) Against An Enemy System Node Effect 2 (Desired) Effect 3 (Undesired) Objective “ Operations that are planned, executed, assessed, and adapted based on a holistic understanding of the operational environment in order to influence or change system behavior or capabilities using the integrated application of selected instruments of power to achieve directed policy aims.” JFCOM JWFC Doctrine Pamphlet 7, Nov 04 Political Political Social Social Military Economic Economic Information Information Infrastructure Infrastructure A B C
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Editor's Notes
- Authors: Lt Col Dave Denhard Assistant Professor Department of Operational Sciences Air Force Institute of Technology Maj Robert Umstead Student, School of Advanced Military Studies Ft. Leavenworth, KS