This document discusses using machine learning to predict testability based on source code metrics. It begins with an introduction to the presenting organization and definitions of testability and machine learning concepts. It then shows how decision trees and other machine learning approaches could be used to predict testability levels (high, medium, low) based on source code metrics like number of interfaces, abstractness, and coupling. As an example, metrics from 9 Java packages were analyzed to build and test a predictive model in the Weka machine learning software. However, the document notes the initial model is simplistic and could be improved by incorporating more metrics related to factors in the testability fishbone diagram.
Multi objective genetic algorithm for regression testing reduction eSAT Journals
Abstract Location based authentication is a new direction in development of authentication techniques in the area of security. In this paper, the geographical position of user is an important attribute for authentication of user. It provides strong authentication as a location characteristic can never be stolen or spoofed. As an effective and popular means for privacy protection data hiding in encrypted image is proposed. In our application we are providing secure message passing facilities for this OTP (One Time Password) and Steganoghaphy techniques are used. This technique is relatively new approach towards information security. Keywords: Location based authentication, GPS device, Image encryption, Cryptography, Steganography, and OTP
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
In this presentation I will show a set of important topics about Software Engineering Empirical Studies that can be useful for increasing quality on your thesis and monographs in general. You can read this presentation and to think about how to do a good experimentation by apply its objectives, validation methods, questions, answers expected, define metrics and measuring it.I will exhibit how the researchers selected the data for avoid case studies in a biased way using a GQM methodology to sort the study in a simpler view as well.
Uncertain Knowledge and Reasoning in Artificial IntelligenceExperfy
Learn how to take informed decisions based on probabilities and expert knowledge
Understand and explore one of the most exciting advances in AI in the last decades.
Many hands-on examples, including Python code.
Check it out: https://www.experfy.com/training/courses/uncertain-knowledge-and-reasoning-in-artificial-intelligence
H2O World - Top 10 Deep Learning Tips & Tricks - Arno CandelSri Ambati
H2O World 2015 - Arno Candel
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
The first lecture of expert system with python course.
Enjoy!
you can find the second lecture here:
https://www.slideshare.net/ahmadhussein45/expert-system-with-python-2
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.
What Every Software Engineer Should Know About Machine Learning - Peter NorvigWithTheBest
I discuss how machine learning has great potential for innovation and how machine learning can be applied to various aspects of technology.
Peter Norvig, Director of Research at Google Inc.
Predictive Performance Testing: Integrating Statistical Tests into Agile Deve...Tom Kleingarn
This presentation was delivered by Tom Kleingarn at HP Software Universe 2010 in Washington DC. It describes basic statistical tests that can be applied to any performance engineering practice to improve accuracy and confidence in your test results.
Multi objective genetic algorithm for regression testing reduction eSAT Journals
Abstract Location based authentication is a new direction in development of authentication techniques in the area of security. In this paper, the geographical position of user is an important attribute for authentication of user. It provides strong authentication as a location characteristic can never be stolen or spoofed. As an effective and popular means for privacy protection data hiding in encrypted image is proposed. In our application we are providing secure message passing facilities for this OTP (One Time Password) and Steganoghaphy techniques are used. This technique is relatively new approach towards information security. Keywords: Location based authentication, GPS device, Image encryption, Cryptography, Steganography, and OTP
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
In this presentation I will show a set of important topics about Software Engineering Empirical Studies that can be useful for increasing quality on your thesis and monographs in general. You can read this presentation and to think about how to do a good experimentation by apply its objectives, validation methods, questions, answers expected, define metrics and measuring it.I will exhibit how the researchers selected the data for avoid case studies in a biased way using a GQM methodology to sort the study in a simpler view as well.
Uncertain Knowledge and Reasoning in Artificial IntelligenceExperfy
Learn how to take informed decisions based on probabilities and expert knowledge
Understand and explore one of the most exciting advances in AI in the last decades.
Many hands-on examples, including Python code.
Check it out: https://www.experfy.com/training/courses/uncertain-knowledge-and-reasoning-in-artificial-intelligence
H2O World - Top 10 Deep Learning Tips & Tricks - Arno CandelSri Ambati
H2O World 2015 - Arno Candel
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
The first lecture of expert system with python course.
Enjoy!
you can find the second lecture here:
https://www.slideshare.net/ahmadhussein45/expert-system-with-python-2
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.
What Every Software Engineer Should Know About Machine Learning - Peter NorvigWithTheBest
I discuss how machine learning has great potential for innovation and how machine learning can be applied to various aspects of technology.
Peter Norvig, Director of Research at Google Inc.
Predictive Performance Testing: Integrating Statistical Tests into Agile Deve...Tom Kleingarn
This presentation was delivered by Tom Kleingarn at HP Software Universe 2010 in Washington DC. It describes basic statistical tests that can be applied to any performance engineering practice to improve accuracy and confidence in your test results.
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.
Software quality improvement expert Jan Princen and XBOSoft CEO Philip Lew discuss the use of Predictive Analytics to prevent software defects in this XBOSoft webinar on Defect Prevention.
Automated testing of software applications using machine learning editedMilind Kelkar
Machine Learning is the next internet. It is the backbone of search engines, driverless car, paperless banking, and facial recognition in forensics. Running automated software tests with lesser human intervention without the risk of schedule delays is now a reality. This presentation will explore several practical machine learning concepts that are being adopted to test software applications.
Peter Zimmerer - Passion For Testing, By Examples - EuroSTAR 2010TEST Huddle
EuroSTAR Software Testing Conference 2010 presentation on Passion For Testing, By Examples by Peter Zimmerer. See more at: http://conference.eurostarsoftwaretesting.com/past-presentations/
Moodle is a very flexible application with a large number of variables and roles. Testing upgrades and changes can be a challenge. This presentation should help attendees focus testing at their own workplace.
Identifying and classifying unknown Network Disruptionjagan477830
Since the evolution of modern technology and with the drastic increase in the scale of network communication more and more network disruptions in traffic and private protocols have been taking place. Identifying and classifying the unknown network disruptions can provide support and even help to maintain the backup systems.
Greens Technologys is the No 1 Best Software Testing Training Institute with placement in Chennai with Certification and Job Placements on both Manual, automation testing training like Selenium, QTP/UFT and Performance Testing Training Courses like LoadRunner and JMeter.
Robust Fault-Tolerant Training Strategy Using Neural Network to Perform Funct...Eswar Publications
This paper is intended to introduce an efficient as well as robust training mechanism for a neural network which can be used for testing the functionality of software. The traditional setup of neural network architecture is used constituting the two phases -training phase and evaluation phase. The input test cases are to be trained in first phase and consequently they behave like normal test cases to predict the output as untrained test cases. The test oracle measures the deviation between the outputs of untrained test cases with trained test cases and authorizes a final decision. Our framework can be applied to systems where number of test cases outnumbers the
functionalities or the system under test is too complex. It can also be applied to the test case development when the modules of a system become tedious after modification.
Annotated Bibliography
.
Guidelines: Annotated Bibliography
Purpose: Explore current literature (collection of writing on a specific topic) to increase
knowledge of leadership in nursing practice.
The annotated bibliography assignment will help students prepare to design and present a poster presentation regarding nursing leadership in practice. The focus is building student knowledge of various leadership roles in nursing (current trends). The assignment also promotes student reflection on development of their own leadership skills.
Students will read the summary of the Institute of Medicine (IOM) “The Future of Nursing: Leading Change, Advancing Health” for baseline identification of leadership roles (posted in Blackboard).
Students will then search the literature to identify and select five (5) nurse leaders, who will be the topic of the annotated bibliography summaries (students must use credible sources when searching literature).
Students may also choose to submit 2 of the 5 annotated bibliography summaries on the following topics:
1. Student Nurse Leaders (2)
2. Current Trends in Nursing Leadership (3)
Each of the five annotated bibliography summaries should be no more than one page, typed, and must include the following:
1. The identified leader’s specific roles & responsibilities
2. The identified leader’s accomplishments
3. Barriers and facilitators to leader achievement of goals
4. Knowledge gained from reading content included in the annotated bibliography summary
Annotated Bibliography Grading Rubric
Criteria
Points Possible
Points Earned
Faculty Comments
Provides a clear description of the identified leader’s role (s) and responsibilities (related to nursing)
20
Provides examples of the leader’s
accomplishments (at least 2 examples)
10
Summarizes barriers inhibiting the leader’s achievement of goals
15
Summarizes facilitators enhancing the leader’s achievement of goals
15
Summary of leadership knowledge gained from reading content included in the annotated bibliography summary
20
Correct grammar/spelling
10
APA format
10
Total
100
[Type text]
30 February 2005 QUEUE rants: [email protected] DARNEDTesting large systems is a daunting task, but there are steps we can take to ease the pain.
T
he increasing size and complexity of software, coupled with concurrency and dis-
tributed systems, has made apparent the ineffectiveness of using only handcrafted
tests. The misuse of code coverage and avoidance of random testing has exacer-
bated the problem. We must start again, beginning with good design (including
dependency analysis), good static checking (including model property checking), and
good unit testing (including good input selection). Code coverage can help select and
prioritize tests to make you more effi cient, as can the all-pairs technique for controlling
the number of confi gurations. Finally, testers can use models to generate test coverage
and good stochastic.
Exploratory testing is an approach to testing that emphasizes the freedom and responsibility of testers to continually optimize the value of their work. It is the process of three mutually supportive activities done in parallel: learning, test design, and test execution. With skill and practice, exploratory testers typically uncover an order of magnitude more problems than when the same amount of effort is spent on procedurally scripted testing. All testers conduct exploratory testing in one way or another, but few know how to do it systematically to obtain the greatest benefits. Even fewer can articulate the process. James Bach looks at specific heuristics and techniques of exploratory testing that will help you get the most from this highly productive approach. James focuses on the skills and dynamics of exploratory testing, and how it can be combined with scripted approaches.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
2. Agenda Who are we? Goal of the Presentation Definition of Testability Some Machine Learning Concepts Predict Testability Conclusion Questions/Answers
7. Some Aphorisms displayed on our Walls “The world is changing very fast. Big will not beat small anymore. It will be the fast beating the slow.” Rupert Murdoch, Chairman and CEO, News Corporation Change is inevitable, stability, and security a myth. Be prepared to change, to anticipate, provoke, participate ... but mostly avoid the subject. Who are we? 4
8. Software Quality Offer X-TRAX Our Test Management tool. Scale Our Code Auditing tool. SQA Service Who are we? 5
9. Goal of the Presentation Show how to use Machine Learning Algorithms in the test management. Based on a toy example, we show how to proceed. Very small example of what we do in our R&D team. We want you to probe this in your company. Who are we? 6
10. Testability Testability as a Set of Factors. ISO defines testability as “attributes of software that bear on the effort needed to validate the software product” [ISO/IEC 9126]. Various factors can contribute to testability (obvious). Which are the factors?
12. Testability Factors [Blinder] Documentation: With regards to testing, requirements and specifications are of prime importance. Implementation: The implementation is the target of all testing, and thus the extent to which the implementation al- lows itself to be tested is a key factor of the testing effort. Testability
13. Testability Factors [Blinder] Test Suite: Factors of the test suite itself also determine the effort required to test. Desirable features of test suites are correctness, automated execution and reuse of test cases. Test Tools: The presence of appropriate test tools can alleviate many problems that originate in other parts of the ‘fish bone’ figure. Process Capability: The organizational structure, staff and resources supporting a certain activity are typically referred to collectively as a (business) process. Properties of the testing process obviously have great influence on the effort required to perform testing. Testability
15. Some Heuristics for Testability Heuristic #1 Reuse Favor modularity before reuse. Its better to have code duplicates than to delay testing of a component because required changes to a superclass or library class it depends on are pending. Heuristic: Give higher priority to the modularity of a system than to the reuse of components. Testability
16. Some Heuristics for Testability Heuristic #2 Loose Coupling Loosely coupled software is one where each of its components has, or makes use of, little or no knowledge of the definitions of other separate components. Heuristic: Reduce the number of used classes. Testability
17. Some Heuristics for Testability Heuristics and Object-Oriented Metrics Testability
18.
19. Interfaces are declared using the interface keyword, and may only contain method signatures and constant declarations.
20. Interfaces cannot be instantiated. A class that implements an interface must implement all of the methods described in the interface.Testability
23. Abstractness = 1 means a completely abstract package.Testability
24.
25. Each class counts only once. Zero if the package does not contain any classes or if external classes do not use the package's classes. Testability
26.
27. Each class counts only once. Zero if the package does not contain any classes or if external classes are not used by the package's classes. Testability
34. What’s Machine Learning ? Machine learning, a branch of artificial intelligence, is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data. Data can be seen as examples that illustrate relations between observed variables. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data. Machine Learning
35. What’s Machine Learning ? Many approaches exist in the Machine Learning world. Neural networks, Bayesian networks, clustering,.. Machine Learning
36. Some ML Approaches – Decision Tree Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. Machine Learning
37. Some ML Approaches – Association Rule Association rule learning is a method for discovering interesting relations between variables in large databases. A typical and widely-used example of association rule mining is Market Basket Analysis. Example: Association rule "If A and B are purchased then C is purchased on the same trip" Machine Learning
38. Some ML Approaches – Neural Network An artificial neural network (ANN), usually called "neural network" (NN), is a mathematical model or computational model that tries to simulate the structure and/or functional aspects of biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. They are usually used to model complex relationships between inputs and outputs or to find patterns in data. Example: Milan Lab: Soccer Club Predict injuries of the soccer player. Machine Learning
39. Some ML Approaches – Clustering Cluster analysis or clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. Clustering is a method of unsupervised learning, and a common technique for statistical data analysis. Machine Learning
40. Some ML Approaches – Clustering A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Machine Learning
41. Machine Learning & Statistics Statistics: focus on understanding data in terms of models Statistics: interpretability, hypothesis testing Machine Learning: greater focus on prediction Machine Learning: focus on the analysis of learning algorithms : not just large dataset. Machine Learning
42. Simple Example - Weather In the weather example, we want to predict the weather conditions to play outside. According to 4 variables (outlook,temperature, humidity, windy), we ask the following question: Can the children play in the garden? Machine Learning
49. Simple Example -Weather Two steps process: Train the machine: build the decision tree based upon a data set. Predict on new data set: load the decision tree and ask for a new data set. Question: Can the children play outside with the following weather conditions: Outlook: rainy Temperature: 59°F Humidity: 89 Windy: false Machine Learning
78. Bibliography [Blinder] R. Binder. Design for testability in object-oriented systems. Comm. of the ACM, 37(9):87–101, 1994. Predicting Class Testability using Object-Oriented Metrics, M. Bruntink and A. van Deursen. Data mining: practical machine learning tools and techniques Par Ian H. Witten,Eibe Frank