This document describes an approach for code coverage based test case selection and prioritization. It presents algorithms for test case selection (TCS) and test case prioritization (TCP) that aim to select a minimal set of test cases that achieve maximum code coverage. The TCS algorithm analyzes test cases and statements to cluster them into outdated, required and surplus test cases. The TCP algorithm then prioritizes the required test cases based on their individual statement coverage to achieve full code coverage with as few test cases as possible. An example application of the algorithms on a sample program is provided, demonstrating a reduction in test suite size.
EXTRACTING THE MINIMIZED TEST SUITE FOR REVISED SIMULINK/STATEFLOW MODELijaia
Test case generation techniques are successfully employed to generate test cases from a formal model. A problem is that as the model evolves, test suites tend to grow in size, making it too costly to execute entire test suites. This paper aims to propose a practical approach to reduce the size of test suites for modified Simulink/Stateflow (SL/SF) model, which is popularly used for modeling software behavior in many industries like automobile manufacturers. The model for describing a system is frequently modified until it is fixed. The proposed technique is capable of extracting the minimized sized test suite in terms of test coverage, by taking into account both the modified and the affected portion of revised SL/SF model. Two real models for the ECUs deployed in a commercial car are used for an empirical study.
Regression testing concentrates on finding defects after a major code change has occurred. Specifically, it
exposes software regressions or old bugs that have reappeared. It is an expensive testing process that has
been estimated to account for almost half of the cost of software maintenance. To improve the regression
testing process, test case prioritization techniques organizes the execution level of test cases. Further, it
gives an improved rate of fault identification, when test suites cannot run to completion.
EXTRACTING THE MINIMIZED TEST SUITE FOR REVISED SIMULINK/STATEFLOW MODELijaia
Test case generation techniques are successfully employed to generate test cases from a formal model. A problem is that as the model evolves, test suites tend to grow in size, making it too costly to execute entire test suites. This paper aims to propose a practical approach to reduce the size of test suites for modified Simulink/Stateflow (SL/SF) model, which is popularly used for modeling software behavior in many industries like automobile manufacturers. The model for describing a system is frequently modified until it is fixed. The proposed technique is capable of extracting the minimized sized test suite in terms of test coverage, by taking into account both the modified and the affected portion of revised SL/SF model. Two real models for the ECUs deployed in a commercial car are used for an empirical study.
Regression testing concentrates on finding defects after a major code change has occurred. Specifically, it
exposes software regressions or old bugs that have reappeared. It is an expensive testing process that has
been estimated to account for almost half of the cost of software maintenance. To improve the regression
testing process, test case prioritization techniques organizes the execution level of test cases. Further, it
gives an improved rate of fault identification, when test suites cannot run to completion.
A Test Analysis Method for Black Box Testing Using AUT and Fault Knowledge.Tsuyoshi Yumoto
With a rapid increase in size and complexity of software today, the scope of software testing is also expanding. The efficiency of software testing needs to be improved in order to ensure the appropriate delivery deadline and cost of software development. For improving efficiency of software testing, the test needs to be designed in a way that the number of test cases is sufficient and appropriate in quantity. Test analysis is the activity to refine Application Under Test (AUT) into proper size that test design techniques can be applied to. It is for designing the test properly. However, the classification for proper size depends on individual’s own judgments. This paper proposes a test analysis method for the black box testing using a test category that is the classification based on fault and AUT knowledge.
Enhanced technique for regression testingeSAT Journals
Abstract
Regression testing perform where to retest modified version of software which already modified by developers on the basis of
Feedback and testing of the software. Regression testing performs where no internal software knowledge and code known by the
tester. Here all test cases not possible to retest this is a problem. So here we will use test case priority technique. that will
provide priority to retest test case first. this test case priority technique uses for increase efficiency of regression testing. Because
high priority test case execute first. and in our proposed technique we are set priority on the basis of [average time for
execution(figure.1).which find out average time for execution on the basis of fault/time. and we will also ignore test cases which
contain same average time for execution and we will select only one test cases in this situation.. After arrange test cases in
descending order by average time of test cases execute test suite. This method gives more prioritization of test cases is compare to
previous techniques. Finally we will get more prioritized test suite. Then we have also checked efficiency of this new method by
APFD Metric [1]. Our primary purpose of this research paper to introduce new prioritization technique which provides better
result is compare to previous techniques. which improves efficiency of Regression testing.
Keywords: Regression Testing, Prioritization, Test Case, Test Suite, APFD Metric[1].
Test Case Optimization and Redundancy Reduction Using GA and Neural Networks IJECEIAES
More than 50% of software development effort is spent in testing phase in a typical software development project. Test case design as well as execution consume a lot of time. Hence, automated generation of test cases is highly required. Here a novel testing methodology is being presented to test objectoriented software based on UML state chart diagrams. In this approach, function minimization technique is being applied and generate test cases automatically from UML state chart diagrams. Software testing forms an integral part of the software development life cycle. Since the objective of testing is to ensure the conformity of an application to its specification, a test “oracle” is needed to determine whether a given test case exposes a fault or not. An automated oracle to support the activities of human testers can reduce the actual cost of the testing process and the related maintenance costs. In this paper, a new concept is being presented using an UML state chart diagram and tables for the test case generation, artificial neural network as an optimization tool for reducing the redundancy in the test case generated using the genetic algorithm. A neural network is trained by the backpropagation algorithm on a set of test cases applied to the original version of the system.
Software testing is an important activity of the software development process. Software testing is most
efforts consuming phase in software development. One would like to minimize the effort and maximize the
number of faults detected and automated test case generation contributes to reduce cost and time effort.
Hence test case generation may be treated as an optimization problem In this paper we have used genetic
algorithm to optimize the test case that are generated applying conditional coverage on source code. Test
case data is generated automatically using genetic algorithm are optimized and outperforms the test cases
generated by random testing.
This presentation provides an overview of such basic terms as "test design" and "test cases", software design testing lifecycle and test design techniques. Next, the focus is set on four black-box techniques, namely Boundary value analysis, Equivalence partitioning, Decision tables and State transition.
This presentation by Tetiana Trushchenko (Test Engineer, Consultant, GlobalLogic), was delivered at GlobalLogic Mykolaiv QA Workshop on July 7, 2018.
Defect prediction models help software quality assurance teams to effectively allocate their limited resources to the most defect-prone software modules. Model validation techniques, such as k-fold cross-validation, use this historical data to estimate how well a model will perform in the future. However, little is known about how accurate the performance estimates of these model validation techniques tend to be. In this paper, we set out to investigate the bias and variance of model validation techniques in the domain of defect prediction. A preliminary analysis of 101 publicly available defect prediction datasets suggests that 77% of them are highly susceptible to producing unstable results. Hence, selecting an appropriate model validation technique is a critical experimental design choice. Based on an analysis of 256 studies in the defect prediction literature, we select the 12 most commonly adopted model validation techniques for evaluation. Through a case study of data from 18 systems that span both open-source and proprietary domains, we derive the following practical guidelines for future defect prediction studies: (1) the single holdout validation techniques should be avoided; and (2) researchers should use the out-of-sample bootstrap validation technique instead of holdout or the commonly-used cross-validation techniques.
Model based test case prioritization using neural network classificationcseij
Model-based testing for real-life software systems often require a large number of tests, all of which cannot
exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in
accordance with their importance the tester perceives. In this paper, this problem is solved by improving
our given previous study, namely, applying classification approach to the results of our previous study
functional relationship between the test case prioritization group membership and the two attributes:
important index and frequency for all events belonging to given group are established. A for classification
purpose, neural network (NN) that is the most advances is preferred and a data set obtained from our study
for all test cases is classified using multilayer perceptron (MLP) NN. The classification results for
commercial test prioritization application show the high classification accuracies about 96% and the
acceptable test prioritization performances are achieved.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Test Analysis Method for Black Box Testing Using AUT and Fault Knowledge.Tsuyoshi Yumoto
With a rapid increase in size and complexity of software today, the scope of software testing is also expanding. The efficiency of software testing needs to be improved in order to ensure the appropriate delivery deadline and cost of software development. For improving efficiency of software testing, the test needs to be designed in a way that the number of test cases is sufficient and appropriate in quantity. Test analysis is the activity to refine Application Under Test (AUT) into proper size that test design techniques can be applied to. It is for designing the test properly. However, the classification for proper size depends on individual’s own judgments. This paper proposes a test analysis method for the black box testing using a test category that is the classification based on fault and AUT knowledge.
Enhanced technique for regression testingeSAT Journals
Abstract
Regression testing perform where to retest modified version of software which already modified by developers on the basis of
Feedback and testing of the software. Regression testing performs where no internal software knowledge and code known by the
tester. Here all test cases not possible to retest this is a problem. So here we will use test case priority technique. that will
provide priority to retest test case first. this test case priority technique uses for increase efficiency of regression testing. Because
high priority test case execute first. and in our proposed technique we are set priority on the basis of [average time for
execution(figure.1).which find out average time for execution on the basis of fault/time. and we will also ignore test cases which
contain same average time for execution and we will select only one test cases in this situation.. After arrange test cases in
descending order by average time of test cases execute test suite. This method gives more prioritization of test cases is compare to
previous techniques. Finally we will get more prioritized test suite. Then we have also checked efficiency of this new method by
APFD Metric [1]. Our primary purpose of this research paper to introduce new prioritization technique which provides better
result is compare to previous techniques. which improves efficiency of Regression testing.
Keywords: Regression Testing, Prioritization, Test Case, Test Suite, APFD Metric[1].
Test Case Optimization and Redundancy Reduction Using GA and Neural Networks IJECEIAES
More than 50% of software development effort is spent in testing phase in a typical software development project. Test case design as well as execution consume a lot of time. Hence, automated generation of test cases is highly required. Here a novel testing methodology is being presented to test objectoriented software based on UML state chart diagrams. In this approach, function minimization technique is being applied and generate test cases automatically from UML state chart diagrams. Software testing forms an integral part of the software development life cycle. Since the objective of testing is to ensure the conformity of an application to its specification, a test “oracle” is needed to determine whether a given test case exposes a fault or not. An automated oracle to support the activities of human testers can reduce the actual cost of the testing process and the related maintenance costs. In this paper, a new concept is being presented using an UML state chart diagram and tables for the test case generation, artificial neural network as an optimization tool for reducing the redundancy in the test case generated using the genetic algorithm. A neural network is trained by the backpropagation algorithm on a set of test cases applied to the original version of the system.
Software testing is an important activity of the software development process. Software testing is most
efforts consuming phase in software development. One would like to minimize the effort and maximize the
number of faults detected and automated test case generation contributes to reduce cost and time effort.
Hence test case generation may be treated as an optimization problem In this paper we have used genetic
algorithm to optimize the test case that are generated applying conditional coverage on source code. Test
case data is generated automatically using genetic algorithm are optimized and outperforms the test cases
generated by random testing.
This presentation provides an overview of such basic terms as "test design" and "test cases", software design testing lifecycle and test design techniques. Next, the focus is set on four black-box techniques, namely Boundary value analysis, Equivalence partitioning, Decision tables and State transition.
This presentation by Tetiana Trushchenko (Test Engineer, Consultant, GlobalLogic), was delivered at GlobalLogic Mykolaiv QA Workshop on July 7, 2018.
Defect prediction models help software quality assurance teams to effectively allocate their limited resources to the most defect-prone software modules. Model validation techniques, such as k-fold cross-validation, use this historical data to estimate how well a model will perform in the future. However, little is known about how accurate the performance estimates of these model validation techniques tend to be. In this paper, we set out to investigate the bias and variance of model validation techniques in the domain of defect prediction. A preliminary analysis of 101 publicly available defect prediction datasets suggests that 77% of them are highly susceptible to producing unstable results. Hence, selecting an appropriate model validation technique is a critical experimental design choice. Based on an analysis of 256 studies in the defect prediction literature, we select the 12 most commonly adopted model validation techniques for evaluation. Through a case study of data from 18 systems that span both open-source and proprietary domains, we derive the following practical guidelines for future defect prediction studies: (1) the single holdout validation techniques should be avoided; and (2) researchers should use the out-of-sample bootstrap validation technique instead of holdout or the commonly-used cross-validation techniques.
Model based test case prioritization using neural network classificationcseij
Model-based testing for real-life software systems often require a large number of tests, all of which cannot
exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in
accordance with their importance the tester perceives. In this paper, this problem is solved by improving
our given previous study, namely, applying classification approach to the results of our previous study
functional relationship between the test case prioritization group membership and the two attributes:
important index and frequency for all events belonging to given group are established. A for classification
purpose, neural network (NN) that is the most advances is preferred and a data set obtained from our study
for all test cases is classified using multilayer perceptron (MLP) NN. The classification results for
commercial test prioritization application show the high classification accuracies about 96% and the
acceptable test prioritization performances are achieved.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Real time implementation of the software system requires being more versatile. In the maintenance phase, the modified system under regression testing must assure that the existing system remains defect free. Test case prioritization technique of regression testing includes code as well as model based methods of prioritizing the test cases. System model based test case prioritization can detect the severe faults early as compare to the code based test case prioritization. Model based prioritization techniques based on requirements in a cost effective manner has not been taken for study so far. Model based testing used to test the functionality of the software system based on requirement. An effective model based approach is defined for prioritizing test cases and to generate the effective test sequence. The test cases are rescheduled based on requirement analysis and user view analysis. With the use of weighted approach the overall cost is estimated to test the functionality of the model elements. Here, the genetic approach has been applied to generate efficient test path. The regression cost in terms of effort has been reduced under model based prioritization approach.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
TEST CASE PRIORITIZATION FOR OPTIMIZING A REGRESSION TESTijfcstjournal
Regression testing makes sure that upgradation of software in terms of adding new features or for bug
fixing purposes should not hamper previously working functionalities. Whenever a software is upgraded or
modified, a set of test cases are run on each of its functions to assure that the change to that function is not
affecting other parts of the software that were previously running flawlessly. For achieving this, all existing
test cases need to run as well as new test cases might be required to be created. It is not feasible to reexecute
every test case for all the functions of a given software, because if there is a large number of test
cases to be run, then a lot of time and effort would be required. This problem can be addressed by
prioritizing test cases. Test case prioritization technique reorders the priority in which test cases are
implemented, in an attempt to ensure that maximum faults are uncovered early on by the high priority test
cases implemented first. In this paper we propose an optimized test case prioritization technique using Ant
Colony Optimization (ACO) to reduce the cost, effort and time taken to perform regression testing and also
uncover maximum faults. Comparison of different techniques such as Retest All, Test Case Minimization,
Test Case Prioritization, Random Test Case Selection and Test Case Prioritization using ACO is also
depicted.
TEST CASE PRIORITIZATION FOR OPTIMIZING A REGRESSION TESTijfcstjournal
Regression testing makes sure that upgradation of software in terms of adding new features or for bug fixing purposes should not hamper previously working functionalities. Whenever a software is upgraded or modified, a set of test cases are run on each of its functions to assure that the change to that function is not affecting other parts of the software that were previously running flawlessly. For achieving this, all existing test cases need to run as well as new test cases might be required to be created. It is not feasible to re- execute every test case for all the functions of a given software, because if there is a large number of test cases to be run, then a lot of time and effort would be required. This problem can be addressed by prioritizing test cases. Test case prioritization technique reorders the priority in which test cases are implemented, in an attempt to ensure that maximum faults are uncovered early on by the high priority test cases implemented first. In this paper we propose an optimized test case prioritization technique using Ant Colony Optimization (ACO) to reduce the cost, effort and time taken to perform regression testing and also uncover maximum faults. Comparison of different techniques such as Retest All, Test Case Minimization, Test Case Prioritization, Random Test Case Selection and Test Case Prioritization using ACO is also depicted.
TEST CASE PRIORITIZATION FOR OPTIMIZING A REGRESSION TESTijfcstjournal
Regression testing makes sure that upgradation of software in terms of adding new features or for bug
fixing purposes should not hamper previously working functionalities. Whenever a software is upgraded or
modified, a set of test cases are run on each of its functions to assure that the change to that function is not
affecting other parts of the software that were previously running flawlessly. For achieving this, all existing
test cases need to run as well as new test cases might be required to be created. It is not feasible to reexecute every test case for all the functions of a given software, because if there is a large number of test
cases to be run, then a lot of time and effort would be required. This problem can be addressed by
prioritizing test cases. Test case prioritization technique reorders the priority in which test cases are
implemented, in an attempt to ensure that maximum faults are uncovered early on by the high priority test
cases implemented first. In this paper we propose an optimized test case prioritization technique using Ant
Colony Optimization (ACO) to reduce the cost, effort and time taken to perform regression testing and also
uncover maximum faults. Comparison of different techniques such as Retest All, Test Case Minimization,
Test Case Prioritization, Random Test Case Selection and Test Case Prioritization using ACO is also
depicted.
TEST CASE PRIORITIZATION FOR OPTIMIZING A REGRESSION TESTijfcstjournal
Regression testing makes sure that upgradation of software in terms of adding new features or for bug
fixing purposes should not hamper previously working functionalities. Whenever a software is upgraded or
modified, a set of test cases are run on each of its functions to assure that the change to that function is not
affecting other parts of the software that were previously running flawlessly. For achieving this, all existing
test cases need to run as well as new test cases might be required to be created. It is not feasible to reexecute
every test case for all the functions of a given software, because if there is a large number of test
cases to be run, then a lot of time and effort would be required. This problem can be addressed by
prioritizing test cases. Test case prioritization technique reorders the priority in which test cases are
implemented, in an attempt to ensure that maximum faults are uncovered early on by the high priority test
cases implemented first. In this paper we propose an optimized test case prioritization technique using Ant
Colony Optimization (ACO) to reduce the cost, effort and time taken to perform regression testing and also
uncover maximum faults. Comparison of different techniques such as Retest All, Test Case Minimization,
Test Case Prioritization, Random Test Case Selection and Test Case Prioritization using ACO is also
depicted.
Configuration Navigation Analysis Model for Regression Test Case Prioritizationijsrd.com
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.
Regression test selection model: a comparison between ReTSE and pythiaTELKOMNIKA JOURNAL
As software systems change and evolve over time regression tests have to be run to validate these changes. Regression testing is an expensive but essential activity in software maintenance. The purpose of this paper is to compare a new regression test selection model called ReTSE with Pythia. The ReTSE model uses decomposition slicing in order to identify the relevant regression tests. Decomposition slicing provides a technique that is capable of identifying the unchanged parts of a system. Pythia is a regression test selection technique based on textual differencing. Both techniques are compare using a Power program taken from Vokolos and Frankl’s paper. The analysis of this comparison has shown promising results in reducing the number of tests to be run after changes are introduced.
A NOVEL APPROACH FOR TEST CASEPRIORITIZATIONIJCSEA Journal
Test case prioritization techniques basically schedule the execution of test cases in a definite order such that to attain an objective function with greater efficiency. This scheduling of test cases improves the results of regression testing. Test case prioritization techniques order the test cases such that the most important ones are executed first encountering the faults first and thus makes the testing effective. In this paper an approach is presented which calculates the product of statement coverage and function calls. The results illustrate the effectiveness of formula computed with the help of APFD metric.
LusRegTes: A Regression Testing Tool for Lustre Programs IJECEIAES
Lustre is a synchronous data-flow declarative language widely used for safety-critical applications (avionics, energy, transport...). In such applications, the testing activity for detecting errors of the system plays a crucial role. During the development and maintenance processes, Lustre programs are often evolving, so regression testing should be performed to detect bugs. In this paper, we present a tool for automatic regression testing of Lustre programs. We have defined an approach to generate test cases in regression testing of Lustre programs. In this approach, a Lustre program is represented by an operator network, then the set of paths is identified and the path activation conditions are symbolically computed for each version. Regression test cases are generated by comparing paths between versions. The approach was implemented in a tool, called LusRegTes, in order to automate the test process for Lustre programs.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Epistemic Interaction - tuning interfaces to provide information for AI support
Code coverage based test case selection and prioritization
1. International Journal of Software Engineering & Applications (IJSEA), Vol.4, No.6, November 2013
CODE COVERAGE BASED TEST CASE SELECTION
AND PRIORITIZATION
R.Beena 1, Dr.S.Sarala 2
1
Research Scholar, Dept. of Information Technology, Bharathiar University, Coimbatore.
2
Assistant Professor, Dept. of Information Technology, Bharathiar University,
Coimbatore
ABSTRACT
Regression Testing is exclusively executed to guarantee the desirable functionality of existing software
after pursuing quite a few amendments or variations in it. Perhaps, it testifies the quality of the modified
software by concealing the regressions or software bugs in both functional and non-functional applications
of the system. In fact, the maintenance of test suite is enormous as it necessitates a big investment of time
and money on test cases on a large scale. So, minimizing the test suite becomes the indispensable requisite
to lessen the budget on regression testing. Precisely, this research paper aspires to present an innovative
approach for the effective selection and prioritization of test cases which in return may procure a maximum
code average.
KEYWORDS
Test Case Selection, Test Case Prioritization, Code Coverage
1. INTRODUCTION
Regression testing is an authentication method pursued in all levels of system and software
testing. Despite ensuring the functioning capacity of the software or system after making
amendments, Regression Testing, exhibits a predominant function with the previously deployed
test codes of the enhanced software. The prime aspiration of running a Regression Test is to
assure that modified or amended component of software does not give way for bugs in the
unaltered portion of the software. The re-execution of test cases are performed to verify that the
previous functionality clubbed with the present changes is desirably functioning.
The various regression testing techniques are test case minimization, test case selection and test
case prioritization .The aim of test case minimization technique is to eliminate the redundant test
cases, while test case selection techniques are performed to reduce the size of a test suite. Test
case prioritization techniques are concerned with ordering of test cases for detection of faults at
the earliest. This paper presents a customized technique for Test case selection and Test case
prioritization.
DOI : 10.5121/ijsea.2013.4604
39
2. International Journal of Software Engineering & Applications (IJSEA), Vol.4, No.6, November 2013
Test case selection implies identifying a smaller subset of test suite from the existing large test
suite [1]. According to [2], test case selection problem is stated subsequently.
Given: The original program, P, the revised version of P, P' and a test suite, T.
Aim: To identify T' ∈T, for the modified version P'
Test Case Prioritization is the process of arranging test cases in an order according to some
criteria. Test case prioritization problem defined by Rothermel et al. [3] is follows:
Given: A test suite, T, the set of permutations of T, PT, a function from PT to the real
numbers, f.
Aim: To identify T′ ∈ PT such that (∀T″) (T″∈PT) (T″≠T′) f (T′) ≥ f (T″)
Here, ‘PT’ represents the set of all possible prioritizations of ‘T’ and ‘f’. The function that is
applied to any such ordering actually yields an award value for the respective ordering.
2. RELATED WORK
Fischer et al. formulated a test case selection problem with the application of Integer
Programming [4]. The variations of the control flow were not discussed in this approach.
Agrawal et al. outlined an exclusive strategy on test case selection with a special perspective to
the discrepancies found in the program slicing techniques [5].
Rothermel and Harrold elucidated regression test case selection techniques based on graph
walking of Control, Program Dependence Graphs [6], and System Dependence Graphs [7]
besides, Control Flow Graphs [8].
Benedusi et al. executed path analysis for test case selection [9]. A testing structure called
TestTube was introduced by Chen et al. [10] which make use of a modification-based method for
selection of test cases.
Leung and White highlighted a firewall technique for regression testing of system integration
[11]. Laski and Szemer offered a technique for test case selection which is based on cluster
identification technique [12].
In [13], [14], Rothermel et al. were the premiers to study test case prioritization predicaments that
paved a way to them to present six different strategies based on the coverage of statement or
branches.
In [15], Li et al. gives empirical study results of two metaheuristic search techniques and three
greedy search techniques applied to six programs for regression test case prioritization.
In [16], Praveen et al. initiated a novel test case prioritization algorithm that calculates average
faults observed per minute.
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3. International Journal of Software Engineering & Applications (IJSEA), Vol.4, No.6, November 2013
A Regression Testing Technique for Test Case Prioritization based on Code Coverage criteria is
recommended by K.K.Aggarwal in [17].
3. TEST CASE SELECTION
The test cases those are available for the existing version of the program is grouped into three
clusters. Those clusters are named as out-dated, required and surplus. The out-dated cluster
contains the test cases that are not required by both the original program and the modified
program. The required test case group consists of the test cases that are required to be executed
for the modified version of the software. The surplus group comprises of test cases that may be
essential for the later versions of P but are not required for the modified version of P i.e. P'. The
algorithm for Test Case Selection (TCS) which is contributed in the previous work [18] is given
in Figure 1.
Algorithm TCS
Input:
- Matrix TCCij representing the test cases and their statements covered
- Vector SDELi representing the statements deleted in P’
- Vector SMODi representing the statements modified in P’
Output :
- Modified Matrix TCCij, Cluster of Test Cases out_datedi, surplusi, requiredi
begin
1. for each statement that belongs to SDELi
Remove the corresponding statements from TCCij.
2. Find the sum of each row.
3. if sum of the row is 0 then
Add the corresponding test case in the vector out_datedi. and
Remove it from TCCij.
4. Find the test cases that do not cover the statements in the vector SMODi,
Add the corresponding test case in the vector surplusi and
Remove it from TCCij.
5. Add the left over test cases in the vector requiredi.
End
Figure 1. Algorithm TCS
4. TEST CASE PRIORITIZATION
The output obtained from algorithm TCS is supplied as input to the algorithm Test Case
Prioritization (TCP) which is described in Figure 2. An example for the steps of the algorithm
TCS and TCP is elucidated in section 5.
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4. International Journal of Software Engineering & Applications (IJSEA), Vol.4, No.6, November 2013
Algorithm TCP
Input:
- Modified Matrix TCCij representing selected test cases and their statements covered
Output :
- Vector TCPi which contains the test cases to achieve 100% code coverage.
begin
1. Find the sum of each row of the matrix TCCij.
2. Select the test case with highest sum and add that test case into the vector TCPi.
3. Remove all the statements covered by that test case.
4. Repeat step1 until all the statements are deleted.
End
Figure 2. Algorithm TCP
5. EXPERIMENTS AND RESULTS
5.1. Test Case Selection
The original version of the program contains 15 statements and 15 test cases. The test cases and
the coverage of the statements by the test cases are represented as a binary matrix. The binary
matrix represented as (TCCij) is given in Table 1.
S1
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
S15
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
S2
Table1. Test cases and statement coverage TCCij
1
1
0
0
1
0
1
0
0
1
0
1
0
0
1
0
0
0
1
0
1
0
1
0
1
0
1
1
1
0
1
0
1
0
1
0
0
1
0
0
0
0
0
0
0
1
1
0
1
0
1
0
0
1
0
1
0
1
0
0
1
0
0
0
1
0
1
0
1
0
0
1
0
0
1
0
1
1
0
1
0
0
1
1
0
0
0
1
0
0
1
0
0
1
0
1
0
0
1
1
0
1
1
0
0
1
1
0
1
0
0
0
0
0
1
1
0
1
0
0
0
1
0
1
0
1
1
1
1
1
0
0
0
0
1
1
1
1
0
1
1
0
0
1
0
1
0
1
0
0
1
0
0
0
0
1
0
1
0
0
0
0
0
0
1
0
0
0
0
0
1
1
1
0
0
0
0
1
0
1
0
1
1
1
0
0
0
0
1
1
1
0
1
0
0
0
0
0
1
1
0
1
0
0
1
0
0
1
0
0
0
0
0
0
1
0
0
0
0
1
0
1
0
0
0
Let us consider, in the modified version of the program the statements S3, S4, S6, S8, S10, S13
have been deleted and the statements S2, S7, S15 have been modified. So the two vectors SDELi
and SMODi are represented as
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5. International Journal of Software Engineering & Applications (IJSEA), Vol.4, No.6, November 2013
SDELi = {S3, S4, S6, S8, S10, S13}
SMODi = {S2, S7, S15}
Table2. Modified TCCij
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
S1
1
1
0
0
1
0
1
0
0
1
0
1
0
0
1
S2
0
0
0
1
0
1
0
1
0
1
0
1
1
1
0
S5
1
0
0
0
1
0
1
0
1
0
0
1
0
0
1
S7
1
0
0
1
0
1
0
0
1
1
0
1
1
0
0
S9
0
1
0
1
0
1
1
1
1
1
0
0
0
0
1
S11
1
0
0
0
0
1
0
1
0
0
0
0
0
0
1
S12
0
0
0
0
0
1
1
1
0
0
0
0
1
0
1
S14
0
0
0
1
1
0
1
0
0
1
0
0
1
0
0
S15
0
0
0
0
1
0
0
0
0
1
0
1
0
0
0
The matrix TCCij is personalized by removing the statements that are available in the vector
group SDELi at the end of the execution of step 1.The modified TCCij is given in Table 2. The
number of statements covered by each test case is calculated according to step 2. For example T1
covers four statements namely S1, S5, S7 and S11.Table 3 represents the total number of
statements covered by each test case.
Table3. Number of statements covered by test cases
Test Cases
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
Statements
Covered
4
2
0
4
4
5
5
4
3
6
0
5
4
1
5
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6. International Journal of Software Engineering & Applications (IJSEA), Vol.4, No.6, November 2013
As given in step 3, the test cases with the sum as zero are removed from the matrix TCCij. Now
the new matrix TCCij is given in Table 4. A new vector out_datedi is created which contains the
removed test cases from TCCij. The vector out-datedi = {T3, T11}
Table4.TCCij without out-datedi
T1
T2
T4
T5
T6
T7
T8
T9
T10
T12
T13
T14
T15
S1
1
1
0
1
0
1
0
0
1
1
0
0
1
S2
0
0
1
0
1
0
1
0
1
1
1
1
0
S5
1
0
0
1
0
1
0
1
0
1
0
0
1
S7
1
0
1
0
1
0
0
1
1
1
1
0
0
S9
0
1
1
0
1
1
1
1
1
0
0
0
1
S11
1
0
0
0
1
0
1
0
0
0
0
0
1
S12
0
0
0
0
1
1
1
0
0
0
1
0
1
S14
0
0
1
1
0
1
0
0
1
0
1
0
0
S15
0
0
0
1
0
0
0
0
1
1
0
0
0
The vector SMODi contains the statements that are modified in the new version of the program
and the test cases that do not cover those statements are removed from TCC ij and inserted into the
cluster surplusi. The new TCCij is given in Table 5. The vector surplusi = {T2, T7, T15}
Table5.TCCij without surplusi
T1
T4
T5
T6
T8
T9
T10
T12
T13
T14
S1
1
0
1
0
0
0
1
1
0
0
S2
0
1
0
1
1
0
1
1
1
1
S5
1
0
1
0
0
1
0
1
0
0
S7
1
1
0
1
0
1
1
1
1
0
S9
0
1
0
1
1
1
1
0
0
0
S11
1
0
0
1
1
0
0
0
0
0
S12
0
0
0
1
1
0
0
0
1
0
S14
0
1
1
0
0
0
1
0
1
0
S15
0
0
1
0
0
0
1
1
0
0
All the remaining test cases that are available in TCCij are inserted into a new cluster group
requiredi as mentioned in step 5.The vector
requiredi = {T1, T4, T5, T6, T8, T9, T10, T12, T13, T14}
The comparison between the original size of the test suite and the reduced size of the test suite is
specified in Figure 3. The result shows that there is a notable reduction in the size between the
two test suites.
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7. International Journal of Software Engineering & Applications (IJSEA), Vol.4, No.6, November 2013
120
100
80
60
40
20
0
Initial Suite
After Test Case
Selection
Test Suite Size
Figure3. Test Suite Size after Selection
5.2. Test Case Prioritization
Input matrix TCCij for Test Case Prioritization is given in Table 5.
Iteration 1:
As given in step 1, the number of statements covered by each test case is counted from the new
matrix TCCij. It is given in Table 6.
Table 6. Number of statements covered by test cases
Test Cases
T1
T4
T5
T6
T8
T9
T10
T12
T13
T14
Statements
Covered
4
4
4
5
4
3
6
5
4
1
As given in step 2, the test case with highest sum is removed from TCCij and that test case is
added into the Test Case Prioritized vector TCPi. The vector TCPi = {T10}.All the statements
that are covered by the test case T10 is removed from TCCij. The modified TCCij is given in
Table 7.
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8. International Journal of Software Engineering & Applications (IJSEA), Vol.4, No.6, November 2013
Table7.Updated TCCij
Iteration 2:
As given in step 1, the sum of each row of the updated matrix TCCij given in Table 7 is computed
and the sum is specified in Table 8.
Table8. Number of statements covered by test cases
Test Cases
T1
T4
T5
T6
T8
T9
T10
T12
T13
T14
Statements
Covered
2
0
1
2
2
1
0
1
1
0
As given in step 2, the test case with highest sum is removed from TCCij and that test case is
added into the vector TCPi. Here in this example, there are three test cases {T1, T6, T8} with
highest sum. The test case T1 is selected here. The issue of equal priority is to be considered in
future. Now the vector TCPi = {T10, T1}. All the statements that are covered by the test case T1
is removed from TCCij. The modified TCCij is given in Table 9.
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9. International Journal of Software Engineering & Applications (IJSEA), Vol.4, No.6, November 2013
Table 9. Updated TCCij
Iteration 3:
As given in step 1, the sum of each row of the updated matrix TCCij given in Table 9 is computed
and the sum is specified in Table 10.
Table 10. Number of statements covered by test cases
Test Cases
T1
T4
T5
T6
T8
T9
T10
T12
T13
T14
Statements
Covered
0
0
0
1
1
0
0
0
1
0
As given in step 2, the test case with highest sum is removed from TCCij and that test case is
added into the vector TCPi. Here in this example, there are three test cases {T6, T8, T13} with
highest sum. The test case T6 is selected here. The final prioritized vector
TCPi = {T10, T1, T6}
Figure4 gives the size of the test suite after test case prioritization. The size of the test suite is
very much reduced and hence the cost of regression testing and time for execution of test cases
can be minimized to a greater extent.
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10. International Journal of Software Engineering & Applications (IJSEA), Vol.4, No.6, November 2013
80
70
60
50
40
30
20
10
0
After Test Case
Selection
After Test Case
Prioritization
Test Suite Size
Figure4. Test Suite Size after Prioritization
6. CONCLUSION
Regression testing is carried out in the maintenance phase of the software development to retest
the software for the revisions it has endured and to confirm the accurate functionalities of the
revised version. A new technique for test case selection and test case prioritization process for
regression testing is proposed in this paper. The proposed technique is very effective in terms of
cost and time involved in regression testing. In future, the regression testing techniques may be
combined with optimization algorithms to contribute more enhanced results.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
Rothermel G, Harrold MJ. A safe, efficient algorithm for regression test selection. Proceedings of
International Conference on Software Maintenance (ICSM 2003), IEEE Computer Society Press,
1993; 358–367.
Rothermel G, Harrold MJ. Analyzing regression test selection techniques. IEEE Transactions on
Software Engineering August 1996; 22(8):529–551.
G. Rothermel, R. Untch, C. Chu, and M.J.Harrold, “Prioritizing Test Cases for Regression Testing,”
IEEE Trans. Software Eng.,vol. 27, no. 10, pp. 929-948, Oct. 2001.
Fischer K. A test case selection method for the validation of software maintenance modifications.
Proceedings of International Computer Software and Applications Conference, IEEE Computer
Society Press, 1977; 421–426.
Agrawal H, Horgan JR, Krauser EW, London SA. Incremental regression testing. Proceedings of the
International Conference on Software Maintenance (ICSM 1993), IEEE Computer Society, 1993;
348–357.
Rothermel G, Harrold MJ. Selecting tests and identifying test coverage requirements for modified
software. Proceedings of International Symposium on Software Testing and Analysis (ISSTA 1994),
ACM Press, 1994; 169–184.
Rothermel G, Harrold MJ. A safe, efficient regression test selection technique. ACM Transactions on
Software Engineering and Methodology April 1997; 6(2):173–210.
48
11. International Journal of Software Engineering & Applications (IJSEA), Vol.4, No.6, November 2013
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
Rothermel G, Harrold MJ. Experience with regression test selection. Empirical Software Engineering:
An International Journal 1997; 2(2):178–188.
Benedusi P, Cmitile A, De Carlini U. Post-maintenance testing based on path change analysis.
Proceedings of the International Conference on Software Maintenance (ICSM 1988), IEEE Computer
Society Press, 1988; 352–361.
Chen YF, Rosenblum D, Vo KP. Testtube: A system for selective regression testing. Proceedings of
the 16th International Conference on Software Engineering (ICSE 1994), ACM Press, 1994; 211–
220.
Leung HKN, White L. Insights into testing and regression testing global variables. Journal of
Software Maintenance 1990; 2(4):209–222.
Laski J, Szermer W. Identification of program modifications and its applications in software
maintenance. Proceedings of the International Conference on Software Maintenance (ICSM 1992),
IEEE Computer Society Press, 1992; 282–290.
G. Rothermel, R. Untch, C. Chu, and M.J. Harrold, “Test Case Prioritization: An Empirical Study,”
Proc. Int’l Conf. Software Maintenance, pp. 179-188, Sept. 1999.
S. Elbaum, A. Malishevsky, and G.Rothermel Test case prioritization: A family of empirical studies.
IEEE Transactions on Software Engineering, February 2002.
Z. Li, M. Harman, and R. M. Hierons. Search Algorithms for Regression Test Case Prioritization,
IEEE Transaction on Software Engineering, vol. 33, no. 4, pp.
225-237, 2007.
Praveen Ranjan Srivastava, Test Case Prioritization, Journal of Theoretical and Applied Information
Technology, pp. 178-181, 2008.
K. K. Aggrawal , Yogesh Singh , A. Kaur, Code coverage based technique for prioritizing test cases
for regression testing, ACM SIGSOFT Software Engineering Notes, v.29 n.5, September 2004.
R. Beena and S. Sarala, A Personalized Approach for Code Coverage based Test Suite Selection,
2012 International Conference on Computer and Software Modeling (ICCSM 2012), IPCSIT vol. 54,
2012.
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