Towards quality of model-based testing in the ioco framework

2013 ◽  
Author(s):  
Michele Volpato ◽  
Jan Tretmans
Keyword(s):  
Model Based ◽  
Model Based Testing

Use of Software Metrics to Improve the Quality of Software Projects Using Regression Testing

2018 ◽  
pp. 204-218
Author(s):  
Arshpreet Kaur Sidhu ◽  
Sumeet Kaur Sehra
Keyword(s):  
Software Metrics ◽  
Early Stage ◽  
Regression Testing ◽  
Stage Model ◽  
Software Projects ◽  
Model Based ◽  
Model Based Testing ◽  
The Cost ◽  
Structural Metrics

Testing of software is broadly divided into three types i.e., code based, model based and specification based. To find faults at early stage, model based testing can be used in which testing can be started from design phase. Furthermore, in this chapter, to generate new test cases and to ensure the quality of changed software, regression testing is used. Early detection of faults will not only reduce the cost, time and effort of developers but also will help finding risks. We are using structural metrics to check the effect of changes made to software. Finally, the authors suggest identifying metrics and analyze the results using NDepend simulator. If results show deviation from standards then again perform regression testing to improve the quality of software.

Use of Software Metrics to Improve the Quality of Software Projects Using Regression Testing

2022 ◽  
pp. 399-411
Author(s):  
Arshpreet Kaur Sidhu ◽  
Sumeet Kaur Sehra
Keyword(s):  
Software Metrics ◽  
Early Stage ◽  
Regression Testing ◽  
Stage Model ◽  
Software Projects ◽  
Model Based ◽  
Model Based Testing ◽  
The Cost ◽  
Structural Metrics

Testing of software is broadly divided into three types i.e., code based, model based and specification based. To find faults at early stage, model based testing can be used in which testing can be started from design phase. Furthermore, in this chapter, to generate new test cases and to ensure the quality of changed software, regression testing is used. Early detection of faults will not only reduce the cost, time and effort of developers but also will help finding risks. We are using structural metrics to check the effect of changes made to software. Finally, the authors suggest identifying metrics and analyze the results using NDepend simulator. If results show deviation from standards then again perform regression testing to improve the quality of software.

scholarly journals Coverage-based Approach for Model-based Testing in Software Product Line

2018 ◽  
Vol 7 (4.15) ◽  
pp. 63 ◽  
Author(s):  
Rabatul Aduni Sulaiman ◽  
Dayang Norhayati A. Jawawi ◽  
Shahliza Abd Halim
Keyword(s):  
Product Quality ◽  
Software Product Line ◽  
Product Line ◽  
Feature Model ◽  
Model Based ◽  
Model Based Testing ◽  
Software Product ◽  
Quality Of Products

Rapid Quality assurance is an important element in software testing in order to produce high quality products in Software Product Line (SPL). One of the testing techniques that can enhance product quality is Model-Based Testing (MBT). Due to MBT effectiveness in terms of reuse and potential to be adapted, this technique has become an efficient approach that is capable to handle SPL requirements. In this paper, the authors present an approach to manage variability and requirements by using Feature Model (FM) and MBT. This paper focuses on modelling the integration towards enhancing product quality and reducing testing effort. Further, the authors considered coverage criteria, including pairwise coverage, all-state coverage, and all-transition coverage, in order to improve the quality of products. For modelling purposes, the authors constructed a mapping model based on variability in FM and behaviour from statecharts. The proposed approach was validated using mobile phone SPL case study. 

scholarly journals A Comparative Evaluation of approaches for Model Testability

2013 ◽  
Vol 9 (1) ◽  
pp. 948-955
Author(s):  
Pourya Nikfard ◽  
Suhaimi Bin Ibrahim ◽  
Babak Darvish Rohani ◽  
Harihodin Bin Selamat ◽  
Mohd Nazri Mahrin
Keyword(s):  
Software Engineering ◽  
Software Design ◽  
Comparative Evaluation ◽  
Design For Testability ◽  
Test Coverage ◽  
Design Models ◽  
Model Based ◽  
Model Based Testing ◽  
Uml Models

Design for testability is a very importantissue in software engineering. It becomes crucial in the case of Model Based Testing where models are generally not tested before using as input of Model Based Testing. The quality of design models (e.g.; UML models), has received less attention, which are main artifacts of any software design. Testability tends to make the validation phase more efficient in exposing faults during testing, and consequently to increase quality of the end-product to meet required specifications. Testability modeling has been researched for many years. Unfortunately, the modeling of a design for testability is often performed after the design is complete. This limits the functional use of the testability model to determining what level of test coverage is available in the design. This information may be useful to help assess whether a product meets the target requirement to achieve a desired level of test coverage, but has little pro-active effect on making the design more testable.

scholarly journals An Evaluation for Model Testability approaches

2013 ◽  
Vol 9 (1) ◽  
pp. 938-947 ◽  
Author(s):  
Pourya Nikfard ◽  
Suhaimi Bin Ibrahim ◽  
Babak Darvish Rohani ◽  
Harihodin Bin Selamat ◽  
Mohd Nazri Mahrin
Keyword(s):  
Software Engineering ◽  
Software Design ◽  
Design For Testability ◽  
Test Coverage ◽  
Design Models ◽  
Validation Phase ◽  
Model Based ◽  
Model Based Testing ◽  
Uml Models

Design for testability is a very important issue in software engineering. It becomes crucial in the case of Model Based Testing where models are generally not tested before using as input of Model Based Testing. The quality of design models (e.g.; UML models), has received less attention, which are main artifacts of any software design. Testability tends to make the validation phase more efficient in exposing faults during testing, and consequently to increase quality of the end-product to meet required specifications. Testability modeling has been researched for many years. Unfortunately, the modeling of a design for testability is often performed after the design is complete. This limits the functional use of the testability model to determining what level of test coverage is available in the design. This information may be useful to help assess whether a product meets the target requirement to achieve a desired level of test coverage, but has little proactive effect on making the design more testable.

Model-Based Testing for Web Applications

2011 ◽  
Vol 34 (6) ◽  
pp. 1012-1028 ◽  
Author(s):  
Huai-Kou MIAO ◽  
Sheng-Bo CHEN ◽  
Hong-Wei ZENG
Keyword(s):  
Web Applications ◽  
Model Based ◽  
Model Based Testing

An interpretable mechanism for personalized recommendation based on cross feature

2021 ◽  
pp. 1-12
Author(s):  
Lv YE ◽  
Yue Yang ◽  
Jian-Xu Zeng
Keyword(s):  
Recommender System ◽  
Online Shopping ◽  
Side Information ◽  
Attention Mechanism ◽  
Personalized Recommendation ◽  
Model Based ◽  
Embedded Model ◽  
Feature Attention ◽  
Match Score

The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent.

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