| 1. |
- Allahviranloo, Tofigh, et al.
(author)
-
On the global solution of a fuzzy linear system
- 2014
-
In: Journal of Fuzzy Set Valued Analysis. - : International Scientific Publications and Consulting Services. - 2193-4169. ; 2014
-
Journal article (other academic/artistic)abstract
- The global solution of a fuzzy linear system contains the crisp vector solution of a real linear system. So discussion about the global solution of a fuzzy linear system with a fuzzy number vector in the right hand side and crisp a coefficient matrix is considered. The advantage of the paper is developing a new algorithm to find the solution of such system by considering a global solution based upon the concept of a convex fuzzy numbers. At first the existence and uniqueness of the solution are introduced and then the related theorems and properties about the solution are proved in details. Finally the method is illustrated by solving some numerical examples.
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| 2. |
- Engstrom, Olof, et al.
(author)
-
Performance Analysis of Deep Anomaly Detection Algorithms for Commercial Microwave Link Attenuation
- 2020
-
In: ICACSIS 2020. - : IEEE. ; , s. 47-52
-
Conference paper (peer-reviewed)abstract
- Highly accurate weather classifiers have recently received a great deal of attention due to their promising applications. An alternative to conventional Weather radars consists of using the measured attenuation data in commercial microwave links (CML) as input to a weather classifier. The design of an accurate weather classifier is challenging due to diverse weather conditions, the absence of predefined features, and specific domain requirements in terms of execution time and detection sensitivity. In addition to this, the quality of the data given as input to the classifier plays a crucial role as it directly impacts the classification output. However, the quality of the measured attenuation data in the CMLs poses a serious concern for different reasons, e.g. the nature of the data itself, the location of each link, and the geographical distance between the links. This mandates the adoption of a data preprocessing step before classification with the purpose to validate the quality of the input data. In this paper, we propose a data preprocessing framework which employs a deep learning model to (i) detect anomalies in the raw data and (ii) validate the measured CML attenuation data by adding quality flags. Moreover, the feasibility and possible generalizations of the proposed framework are studied by conducting an empirical case study performed on real data collected from CMLs at Ericsson AB in Sweden. The empirical evaluation indicates that the average area under the receiver operating characteristic curve exceeding 0.72 using the proposed data preprocessing framework.
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| 3. |
- Felderer, Michael, 1978-, et al.
(author)
-
Artificial Intelligence Techniques in System Testing
- 2023
-
In: Optimising the software development process with artificial intelligence. - : Springer Science and Business Media Deutschland GmbH. - 9789811999475 - 9789811999482 ; , s. 221-240
-
Book chapter (other academic/artistic)abstract
- System testing is essential for developing high-quality systems, but the degree of automation in system testing is still low. Therefore, there is high potential for Artificial Intelligence (AI) techniques like machine learning, natural language processing, or search-based optimization to improve the effectiveness and efficiency of system testing. This chapter presents where and how AI techniques can be applied to automate and optimize system testing activities. First, we identified different system testing activities (i.e., test planning and analysis, test design, test execution, and test evaluation) and indicated how AI techniques could be applied to automate and optimize these activities. Furthermore, we presented an industrial case study on test case analysis, where AI techniques are applied to encode and group natural language into clusters of similar test cases for cluster-based test optimization. Finally, we discuss the levels of autonomy of AI in system testing.
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| 4. |
- Källberg, Eva, et al.
(author)
-
Induction of S100A9 homodimer formation in vivo
- 2018
-
In: Biochemical and Biophysical Research Communications. - : Elsevier BV. - 0006-291X. ; , s. 564-568
-
Journal article (peer-reviewed)abstract
- We show here that increased S100A8 and S100A9 protein expression is induced in spleen of animals with active inflammation or with inoculated tumors. In tumor bearing animals an increased expression was also detected in the lung. To further analyze the induced proteins, we performed chemical cross-linking followed by Western blotting. We observed in protein extracts from spleen that both S100A8/S100A9 heterodimers as well as S100A9 homodimers were formed, both after tumor and inflammatory challenge. The cellular source for S100A9 homodimers were CD11b+GR1+ cells. S100A9 homodimers were also secreted into the extracellular space. Lastly, in the spleen from normal and tumor bearing animals cells expressing relatively higher levels of S100A9 compared to S100A8 could be observed by immunohistochemistry. Taken together, these data show that the biologically potent dimeric form of S100A9 is induced in vivo in situations of tumor burden or inflammatory challenge.
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| 5. |
- Landin, Cristina, 1984-, et al.
(author)
-
A Dynamic Threshold Based Approach for Detecting the Test Limits
- 2021
-
In: Sixteenth International Conference on Software Engineering Advances (ICSEA 2021). - : International Academy, Research, and Industry Association (IARIA). - 9781612088945 ; , s. 71-80
-
Conference paper (peer-reviewed)abstract
- Finding a balance between meeting the testing goals and testing resources is always a challenging task. Therefore, employing Machine Learning (ML) techniques for test optimization purposes has received a great deal of attention. However, utilizing ML techniques requires frequently large volumes of data to obtain reliable results. Since the data gathering is hard and also expensive, reducing unnecessary failure or retest in a testing process might end up minimizing the testing resources. Final test yield is a proper performance metric to measure the potential risks influencing certain failure rates. Typically, production determines the yield’s minimum threshold based on an empirical value given by the subject matter experts. However, those thresholds cannot monitor the yield’s fluctuations beyond the acceptable thresholds, which might cause potential failures in consecutive tests. Furthermore, defining the empirical thresholds as either too tight or too loose in production is one of the main causes of yield dropping in the testing process. In this paper, we propose an ML-based solution that detects the divergent yield points based on the prediction and raises a flag depending on the yield class to the testers when a divergent point is above a data-driven threshold. This flexibility enables engineers to have a quantifiable tool to measure to what extend the different changes in the production process are affecting the product performance and execute actions before they occur. The feasibility of the proposed solution is studied by an empirical evaluation, which has been performed on a Telecom use-case at Ericsson in Sweden and tested in two of the latest radio technologies, 4G and 5G.
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| 6. |
- Landin, Cristina, 1984-
(author)
-
AI-Based Methods For Improved Testing of Radio Base Stations : A Case Study Towards Intelligent Manufacturing
- 2023
-
Licentiate thesis (other academic/artistic)abstract
- Testing of complex systems may often require the use of tailored-made solutions, expensive testing equipment, large computing capacity, and manual implementation work due to domain uniqueness. The aforementioned test resources are expensive and time-consuming, which makes them good candidates to optimize. A radio base station (RBS) is a complex system. Upon the arrival of new RBS generations, new testing challenges have been introduced that traditional methods cannot cope with. In order to optimize the test process of RBSs, product quality and production efficiency can be studied.Despite that AI techniques are valuable tools for monitoring behavioral changes in various applications, there have not been sufficient research efforts spent on the use of intelligent manufacturing in already existing factories and production lines. The concept of intelligent manufacturing involves the whole system development life-cycle, such as design, production, and maintenance. Available literature about optimization and integration of industrial applications using AI techniques has not resulted in common solutions due to the complexity of the real-world applications, which have their own unique characteristics, e.g., multivariate, non-linear, non-stationary, multi-modal, class imbalance; making it challenging to find generalizable solutions. This licentiate thesis aims to bridge the gap between theoretical approaches and the implementation of real industrial applications. In this licentiate thesis, two questions are explored, namely how well AI techniques can perform and optimize fault detection and fault prediction on the production of RBSs, as well as how to modify learning algorithms in order to perform transfer learning between different products. These questions are addressed by using different AI techniques for test optimization purposes and are examined in three empirical studies focused on parallel test execution, fault detection and prediction, and automated fault localization. For the parallel test execution study, two different approaches were used to find and cluster semantically similar test cases and propose their execution in parallel. For this purpose, Levenshstein distance and two NLP techniques are compared. The results show that cluster-based test scenarios can be automatically generated from requirement specifications and the execution of semantically similar tests can reduce the number of tests by 95\% in the study case if executed in parallel. Study number two investigates the possibility of predicting testing performance outcomes by analyzing anomalies in the test process and classifying them by their compliance with dynamic test limits instead of fixed limits. The performance measures can be modeled using historical data through regression techniques and the classification of the anomalies is learned using support vector machines and convolutional neural networks. The results show good agreement between the actual and predicted learned model, where the root-mean-square error reaches 0.00073. Furthermore, this approach can automatically label the incoming tests according to the dynamic limits, making it possible to predict errors in an early stage of the process. This study contributes to product quality by monitoring the test measurements beyond fixed limits and contributes to making a more efficient testing process by detecting faults before they are measured. Moreover, study two considers the possibility of using transfer learning due to an insufficient number of anomalies in a single product. The last study focuses on root cause analysis by analyzing test dependencies between test measurements using two known correlation-based methods and mutual information to find strength associations between measurements. The contributions of this study are twofold. First, test dependencies between measurements can be found using Pearson and Spearman correlation and MI; and their dependencies can be linear or higher order. Second, by clustering the associated tests, redundant tests are found, which could be used to update the test execution sequence and choose to execute only the relevant tests, hence, making a more efficient production process by saving test time.
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| 7. |
- Landin, Cristina, 1984-, et al.
(author)
-
Cluster-Based Parallel Testing Using Semantic Analysis
- 2020
-
In: 2020 IEEE International Conference On Artificial Intelligence Testing (AITest). - : IEEE. - 9781728169842 ; , s. 99-106
-
Conference paper (peer-reviewed)abstract
- Finding a balance between testing goals and testing resources can be considered as a most challenging issue, therefore test optimization plays a vital role in the area of software testing. Several parameters such as the objectives of the tests, test cases similarities and dependencies between test cases need to be considered, before attempting any optimization approach. However, analyzing corresponding testing artifacts (e.g. requirement specification, test cases) for capturing the mentioned parameters is a complicated task especially in a manual testing procedure, where the test cases are documented as a natural text written by a human. Thus, utilizing artificial intelligence techniques in the process of analyzing complex and sometimes ambiguous test data, is considered to be working in different industries. Test scheduling is one of the most popular and practical ways to optimize the testing process. Having a group of test cases which are required the same system setup, installation or testing the same functionality can lead to a more efficient testing process. In this paper, we propose, apply and evaluate a natural language processing-based approach that derives test cases' similarities directly from their test specification. The proposed approach utilizes the Levenshtein distance and converts each test case into a string. Test cases are then grouped into several clusters based on their similarities. Finally, a set of cluster-based parallel test scheduling strategies are proposed for execution. The feasibility of the proposed approach is studied by an empirical evaluation that has been performed on a Telecom use-case at Ericsson in Sweden and indicates promising results.
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| 8. |
- Landin, Cristina, 1984-, et al.
(author)
-
Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases
- 2020
-
In: The Fifteenth International Conference on Software Engineering Advances. - : International Academy, Research and Industry Association (IARIA). - 9781612088273 ; , s. 90-97
-
Conference paper (peer-reviewed)abstract
- Software testing is still heavily dependent on human judgment since a large portion of testing artifacts, such as requirements and test cases are written in a natural text by experts. Identifying and classifying relevant test cases in large test suites is a challenging and also time-consuming task. Moreover, to optimize the testing process test cases should be distinguished based on their properties, such as their dependencies and similarities. Knowing the mentioned properties at an early stage of the testing process can be utilized for several test optimization purposes, such as test case selection, prioritization, scheduling,and also parallel test execution. In this paper, we apply, evaluate, and compare the performance of two deep learning algorithmsto detect the similarities between manual integration test cases. The feasibility of the mentioned algorithms is later examined in a Telecom domain by analyzing the test specifications of five different products in the product development unit at Ericsson AB in Sweden. The empirical evaluation indicates that utilizing deep learning algorithms for finding the similarities between manual integration test cases can lead to outstanding results.
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| 9. |
- Saadatmand, Mehrdad, et al.
(author)
-
A Fuzzy Decision Support Approach for Model-Based Tradeoff Analysis of Non-Functional Requirements
- 2015
-
In: 12th International Conference on Information Technology. - Las Vegas, United States. - 9781479988273 ; , s. 112-121, s. 112-121
-
Conference paper (peer-reviewed)abstract
- One of the main challenges in addressing Non-Functional Requirements (NFRs) in designing systems is to take into account their interdependencies and mutual impacts. For this reason, they cannot be considered in isolation and a careful balance and tradeoff among them should be established. This makes it a difficult task to select design decisions and features that lead to the satisfaction of all different NFRs in the system, which becomes even more difficult when the complexity of a system grows. In this paper, we introduce an approach based on fuzzy logic and decision support systems that helps to identify different design alternatives that lead to higher overall satisfaction of NFRs in the system. This is achieved by constructing a model of the NFRs and then performing analysis on the model. To build the model, we use a modified version of the NFR UML profile which we have introduced in our previous works, and using model transformation techniques we automate the analysis of the model.
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| 10. |
- Tahvili, Sahar
(author)
-
A Decision Support System for Integration Test Selection
- 2016
-
Licentiate thesis (other academic/artistic)abstract
- Software testing generally suffers from time and budget limitations. Indiscriminately executing all available test cases leads to sub-optimal exploitation of testing resources. Selecting too few test cases for execution on the other hand might leave a large number of faults undiscovered. Test case selection and prioritization techniques can lead to more efficient usage of testing resources and also early detection of faults. Test case selection addresses the problem of selecting a subset of an existing set of test cases, typically by discarding test cases that do not add any value in improving the quality of the software under test. Test case prioritization schedules test cases for execution in an order to increase their effectiveness at achieving some performance goals such as: earlier fault detection, optimal allocation of testing resources and reducing overall testing effort. In practice, prioritized selection of test cases requires the evaluation of different test case criteria, and therefore, this problem can be formulated as a multi-criteria decision making problem. As the number of decision criteria grows, application of a systematic decision making solution becomes a necessity. In this thesis, we propose a tool-supported framework using a decision support system, for prioritizing and selecting integration test cases in embedded system development. The framework provides a complete loop for selecting the best candidate test case for execution based on a finite set of criteria. The results of multiple case studies, done on a train control management subsystem from Bombardier Transportation AB in Sweden, demonstrate how our approach helps to select test cases in a systematic way. This can lead to early detection of faults while respecting various criteria. Also, we have evaluated a customized return on investment metric to quantify the economic benefits in optimizing system integration testing using our framework.
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| 11. |
- Tahvili, Sahar, et al.
(author)
-
A novel methodology to classify test cases using natural language processing and imbalanced learning
- 2020
-
In: Engineering applications of artificial intelligence. - : Elsevier Ltd. - 0952-1976 .- 1873-6769. ; 95
-
Journal article (peer-reviewed)abstract
- Detecting the dependency between integration test cases plays a vital role in the area of software test optimization. Classifying test cases into two main classes – dependent and independent – can be employed for several test optimization purposes such as parallel test execution, test automation, test case selection and prioritization, and test suite reduction. This task can be seen as an imbalanced classification problem due to the test cases’ distribution. Often the number of dependent and independent test cases is uneven, which is related to the testing level, testing environment and complexity of the system under test. In this study, we propose a novel methodology that consists of two main steps. Firstly, by using natural language processing we analyze the test cases’ specifications and turn them into a numeric vector. Secondly, by using the obtained data vectors, we classify each test case into a dependent or an independent class. We carry out a supervised learning approach using different methods for handling imbalanced datasets. The feasibility and possible generalization of the proposed methodology is evaluated in two industrial projects at Bombardier Transportation, Sweden, which indicates promising results. © 2020 The Authors
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| 12. |
- Tahvili, Sahar
(author)
-
An Online Decision Support Framework for Integration Test Selection and Prioritization (Doctoral Symposium)
- 2016
-
In: The International Symposium on Software Testing and Analysis (ISSTA'16).
-
Conference paper (peer-reviewed)abstract
- Test case prioritization and selection techniques can lead to early detection of faults and can also enable more efficient usage of testing resources. The available methods of test case selection and prioritization suffer from one or several weaknesses. For example, most of them are only applicable at unit level and do not consider the increasing complexity when subsystems get integrated, especially in the context of embedded system development. Furthermore, the existing methods do not take into account results of current test execution to identify and optimize order for rest of the current execution (i.e., they are not online). In this paper, we propose a tool-supported framework, as an online decision support system (DSF), for prioritizing and selecting integration test cases for embedded system development. DSF provides a complete loop for selecting the best candidate test case for execution based on a finite set of criteria. The results of multiple case studies, done on a train control management subsystem from Bombardier Transportation (BT) in Sweden, demonstrate how our approach helps in a systematic way to select test cases such that it can lead to early detection of faults while respecting various criteria. We are also working towards proposing a customized return on investment (ROI) metric to quantify the economic benefits in optimizing system integration testing using our proposed DSF.
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| 13. |
- Tahvili, Sahar, et al.
(author)
-
Artificial Intelligence Methods for Optimization of the Software Testing Process
- 2022
-
Book (other academic/artistic)abstract
- Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way. As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how's, what's and whys. To learn more about Elsevier’s Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier.com/books-and-journals/book-series/uncertainty-computational-techniques-and-decision-intelligence
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| 14. |
- Tahvili, Sahar, et al.
(author)
-
Automated functional dependency detection between test cases using Doc2Vec and Clustering
- 2019
-
In: Proceedings - 2019 IEEE International Conference on Artificial Intelligence Testing, AITest 2019. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728104928 ; , s. 19-26
-
Conference paper (peer-reviewed)abstract
- Knowing about dependencies and similarities between test cases is beneficial for prioritizing them for cost-effective test execution. This holds especially true for the time consuming, manual execution of integration test cases written in natural language. Test case dependencies are typically derived from requirements and design artifacts. However, such artifacts are not always available, and the derivation process can be very time-consuming. In this paper, we propose, apply and evaluate a novel approach that derives test cases' similarities and functional dependencies directly from the test specification documents written in natural language, without requiring any other data source. Our approach uses an implementation of Doc2Vec algorithm to detect text-semantic similarities between test cases and then groups them using two clustering algorithms HDBSCAN and FCM. The correlation between test case text-semantic similarities and their functional dependencies is evaluated in the context of an on-board train control system from Bombardier Transportation AB in Sweden. For this system, the dependencies between the test cases were previously derived and are compared to the results our approach. The results show that of the two evaluated clustering algorithms, HDBSCAN has better performance than FCM or a dummy classifier. The classification methods' results are of reasonable quality and especially useful from an industrial point of view. Finally, performing a random undersampling approach to correct the imbalanced data distribution results in an F1 Score of up to 75% when applying the HDBSCAN clustering algorithm.
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| 15. |
- Tahvili, Sahar, et al.
(author)
-
Cluster-Based Test Scheduling Strategies Using Semantic Relationships between Test Specifications
- 2018
-
In: 5th International Workshop on Requirements Engineering and Testing RET'18. - New York, NY, USA : ACM. - 9781450357494 ; , s. 1-4
-
Conference paper (peer-reviewed)abstract
- One of the challenging issues in improving the test efficiency is that of achieving a balance between testing goals and testing resources. Test execution scheduling is one way of saving time and budget, where a set of test cases are grouped and tested at the same time. To have an optimal test execution schedule, all related information of a test case (e.g. execution time, functionality to be tested, dependency and similarity with other test cases) need to be analyzed. Test scheduling problem becomes more complicated at high-level testing, such as integration testing and especially in manual testing procedure. Test specifications at high-level are generally written in natural text by humans and usually contain ambiguity and uncertainty. Therefore, analyzing a test specification demands a strong learning algorithm. In this position paper, we propose a natural language processing (NLP) based approach that, given test specifications at the integration level, allows automatic detection of test cases’ semantic dependencies. The proposed approach utilizes the Doc2Vec algorithm and converts each test case into a vector in n-dimensional space. These vectors are then grouped using the HDBSCAN clustering algorithm into semantic clusters. Finally, a set of cluster-based test scheduling strategies are proposed for execution. The proposed approach has been applied in a sub-system from the railway domain by analyzing an ongoing testing project at Bombardier Transportation AB, Sweden.
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| 16. |
- Tahvili, Sahar, et al.
(author)
-
Cost-Benefit Analysis of Using Dependency Knowledge at Integration Testing
- 2016. - 7
-
In: Product-Focused Software Process Improvement. - Cham : Springer International Publishing. - 9783319490939 - 9783319490946 ; , s. 268-284, s. 268-284
-
Conference paper (peer-reviewed)abstract
- In software system development, testing can take considerable time and resources, and there are numerous examples in the literature of how to improve the testing process. In particular, methods for selection and prioritization of test cases can play a critical role in efficient use of testing resources. This paper focuses on the problem of selection and ordering of integration-level test cases. Integration testing is performed to evaluate the correctness of several units in composition. Further, for reasons of both effectiveness and safety, many embedded systems are still tested manually. To this end, we propose a process, supported by an online decision support system, for ordering and selection of test cases based on the test result of previously executed test cases. To analyze the economic efficiency of such a system, a customized return on investment (ROI) metric tailored for system integration testing is introduced. Using data collected from the development process of a large-scale safety-critical embedded system, we perform Monte Carlo simulations to evaluate the expected ROI of three variants of the proposed new process. The results show that our proposed decision support system is beneficial in terms of ROI at system integration testing and thus qualifies as an important element in improving the integration testing process.
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| 17. |
- Tahvili, Sahar, et al.
(author)
-
Dynamic Integration Test Selection Based on Test Case Dependencies
- 2016
-
In: 2016 IEEE NINTH INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VERIFICATION AND VALIDATION WORKSHOPS (ICSTW). - Chicago, United States. - 9781509036745 ; , s. 277-286
-
Conference paper (peer-reviewed)abstract
- Prioritization, selection and minimization of test cases are well-known problems in software testing. Test case prioritization deals with the problem of ordering an existing set of test cases, typically with respect to the estimated likelihood of detecting faults. Test case selection addresses the problem of selecting a subset of an existing set of test cases, typically by discarding test cases that do not add any value in improving the quality of the software under test. Most existing approaches for test case prioritization and selection suffer from one or several drawbacks. For example, they to a large extent utilize static analysis of code for that purpose, making them unfit for higher levels of testing such as integration testing. Moreover, they do not exploit the possibility of dynamically changing the prioritization or selection of test cases based on the execution results of prior test cases. Such dynamic analysis allows for discarding test cases that do not need to be executed and are thus redundant. This paper proposes a generic method for prioritization and selection of test cases in integration testing that addresses the above issues. We also present the results of an industrial case study where initial evidence suggests the potential usefulness of our approach in testing a safety-critical train control management subsystem.
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| 18. |
- Tahvili, Sahar, et al.
(author)
-
ESPRET : A tool for execution time estimation of manual test cases
- 2018
-
In: Journal of Systems and Software. - : Elsevier BV. - 0164-1212 .- 1873-1228. ; 146, s. 26-41
-
Journal article (peer-reviewed)abstract
- Manual testing is still a predominant and an important approach for validation of computer systems, particularly in certain domains such as safety-critical systems. Knowing the execution time of test cases is important to perform test scheduling, prioritization and progress monitoring. In this work, we present, apply and evaluate ESPRET (EStimation and PRediction of Execution Time) as our tool for estimating and predicting the execution time of manual test cases based on their test specifications. Our approach works by extracting timing information for various steps in manual test specification. This information is then used to estimate the maximum time for test steps that have not previously been executed, but for which textual specifications exist. As part of our approach, natural language parsing of the specifications is performed to identify word combinations to check whether existing timing information on various test steps is already available or not. Since executing test cases on the several machines may take different time, we predict the actual execution time for test cases by a set of regression models. Finally, an empirical evaluation of the approach and tool has been performed on a railway use case at Bombardier Transportation (BT) in Sweden.
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| 19. |
- Tahvili, Sahar, et al.
(author)
-
Functional Dependency Detection for Integration Test Cases
- 2018
-
In: Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2018. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538678398 ; , s. 207-214
-
Conference paper (peer-reviewed)abstract
- This paper presents a natural language processing (NLP) based approach that, given software requirements specification, allows the functional dependency detection between integration test cases. We analyze a set of internal signals to the implemented modules for detecting dependencies between requirements and thereby identifying dependencies between test cases such that: module 2 depends on module 1 if an output internal signal from module 1 enters as an input internal signal to the module 2. Consequently, all requirements (and thereby test cases) for module 2 are dependent on all the designed requirements (and test cases) for module 1. The dependency information between requirements (and thus corresponding test cases) can be utilized for test case prioritization and scheduling. We have implemented our approach as a tool and the feasibility is evaluated through an industrial use case in the railway domain at Bombardier Transportation (BT), Sweden. © 2018 IEEE.
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| 20. |
- Tahvili, Sahar
(author)
-
Multi-Criteria Optimization of System Integration Testing
- 2018
-
Doctoral thesis (other academic/artistic)abstract
- Optimizing software testing process has received much attention over the last few decades. Test optimization is typically seen as a multi-criteria decision making problem. One aspect of test optimization involves test selection, prioritization and execution scheduling. Having an efficient test process can result in the satisfaction of many objectives such as cost and time minimization. It can also lead to on-time delivery and a better quality of the final software product. To achieve the goal of test efficiency, a set of criteria, having an impact on the test cases, need to be identified. The analysis of several industrial case studies and also state of the art in this thesis, indicate that the dependency between integration test cases is one such criterion, with a direct impact on the test execution results. Other criteria of interest include requirement coverage and test execution time. In this doctoral thesis, we introduce, apply and evaluate a set of approaches and tools for test execution optimization at industrial integration testing level in embedded software development. Furthermore, ESPRET (Estimation and Prediction of Execution Time) and sOrTES (Stochastic Optimizing of Test Case Scheduling) are our proposed supportive tools for predicting the execution time and the scheduling of manual integration test cases, respectively. All proposed methods and tools in this thesis, have been evaluated at industrial testing projects at Bombardier Transportation (BT) in Sweden. As a result of the scientific contributions made in this doctoral thesis, employing the proposed approaches has led to an improvement in terms of reducing redundant test execution failures of up to 40% with respect to the current test execution approach at BT. Moreover, an increase in the requirements coverage of up to 9.6% is observed at BT. In summary, the application of the proposed approaches in this doctoral thesis has shown to give considerable gains by optimizing test schedules in system integration testing of embedded software development.
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| 21. |
- Tahvili, Sahar, et al.
(author)
-
Multi-Criteria Test Case Prioritization Using Fuzzy Analytic Hierarchy Process
- 2015
-
In: ICSEA 2015. - 9781612084381 ; , s. 290-296
-
Conference paper (peer-reviewed)abstract
- One of the key challenges in software testing today is prioritizing and evaluating test cases. The decision of which test cases to design, select and execute first is of great importance, in particular considering that testing is often done late in the implementation process, and therefore needs to be done within tight resource constraints on time and budget. In practice, prioritized selection of test cases requires the evaluation of different test case criteria, and therefore, test case prioritization can be formulated as a multi-criteria decision making problem. As the number of decision criteria grows, application of a systematic decision making solution becomes a necessity. In this paper we propose an approach for prioritized selection of test cases by using the Analytic Hierarchy Process (AHP) technique. To improve the practicality of the approach in real world scenarios, we apply AHP in fuzzy environment so that criteria values can be specified using fuzzy variables than requiring precise quantified values. One of the advantages of the decision making process it that the defined criteria with the biggest and most critical role in priority ranking of test cases is identified. We have applied our approach on an example case in which several test cases for testing non-functional requirements in a systems are defined.
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| 22. |
- Tahvili, Sahar, et al.
(author)
-
Paquinimod prevents development of diabetes in the non-obese diabetic (NOD) mouse
- 2018
-
In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 13:5
-
Journal article (peer-reviewed)abstract
- Quinoline-3-carboxamides (Q compounds) are immunomodulatory compounds that have shown efficacy both in autoimmune disease and cancer. We have in here investigated the impact of one such compound, paquinimod, on the development of diabetes in the NOD mouse model for type I diabetes (T1D). In cohorts of NOD mice treated with paquinimod between weeks 10 to 20 of age and followed up until 40 weeks of age, we observed dose-dependent reduction in incidence of disease as well as delayed onset of disease. Further, in contrast to untreated controls, the majority of NOD mice treated from 15 weeks of age did not develop diabetes at 30 weeks of age. Importantly, these mice displayed significantly less insulitis, which correlated with selectively reduced number of splenic macrophages and splenic Ly6Chi inflammatory monocytes at end point as compared to untreated controls. Collectively, these results demonstrate that paquinimod treatment can significantly inhibit progression of insulitis to T1D in the NOD mouse. We propose that the effect of paquinimod on disease progression may be related to the reduced number of these myeloid cell populations. Our finding also indicates that this compound could be a candidate for clinical development towards diabetes therapy in humans.
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| 23. |
- Tahvili, Sahar
(author)
-
S100A9 in inflammatory disease: a potential target for amelioration
- 2018
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Doctoral thesis (other academic/artistic)abstract
- The quinoline-3-carboxamides (Q compounds) are a family of small molecules with immunomodulatory functions that have shown efficacy in various murine models of inflammatory diseases. One such compound has demonstrated antitumor effects in murine models. Q compounds bind to S100A9, thereby preventing its ligation to TLR4 and RAGE. S100A9 is a small molecule which has shown to have a role in the establishment of tumors.In the first part of this thesis (paper I and II), effects of Q compounds during inflammation and cancer, with the focus on myeloid cells has been investigated. While, the focus of the second part (paper III and IV) was on the molecule S100A9. Given the important role of S100A9 during tumorigenesis, the aim was to evaluate the induction and expression of this molecule in vivo in the context of cancer.In paper I, the effect of the Q compound tasquinimod was evaluated on myeloid cells in a mouse mammary carcinoma tumor. Short-term treatment reduced the accumulation of inflammatory monocytes in the tumors. Depletion of this cell population using an anti-Gr1 antibody resulted in the comparable anti-tumor effect as treatment with tasquinimod during the first few days of tumor growth. Furthermore, long-term tasquinimod treatment reduced myeloid cell expansion in the spleen and made the frequency of precursor cells in the spleen of tumor-bearing mice resemble the naïve state.In paper II, the effect of the Q compound paquinimod was studied in the spontaneous mouse model of type 1 diabetes (NOD mouse). Paquinimod was given to the NOD mice in drinking water in two different protocols: short-term and longterm treatment and disease development were monitored weekly. Paquinimod induced a dose-dependent reduction in the incidence of diabetes and delayed the onset of disease in both treatment strategies. Interestingly, the treated mice showed less destructed islets in their pancreas. Moreover, the treatment reduced number of splenic inflammatory monocytes and macrophages.In paper III, the formation of S100A9 homodimer under inflammatory conditions and cancer was investigated. The cellular source of S100A9 homodimer was shown to be CD11b+ Gr1+ cells. Given the fact that in order to act as a DAMP, S100A9 should reach extracellular space, the presence of S100A9 homodimer in the extracellular milieu was shown. The presence of cells expressing only S100A9, and not both S100A8 and S100A9 was shown in spleens of tumor-bearing animals.In paper IV, the conditions that lead to de novo expression of S100A9 were studied. It was shown that in vivo environment induces S100A9 expression, and this induction is so dependent to this milieu that it was rapidly downregulated after removal of the cells from in vivo. Hypoxia in tumor microenvironment promotes tumor progression and survival and do so mainly by the activity of HIF-1 transcription factor which regulates the expression of many genes involved in the process of tumorigenesis. However, providing hypoxic condition was not sufficient for the induction of S100A9 expression in vitro. Combination of HIF-1α (one component of the transcription factor HIF-1) stabilizer and cytokines did not induce S100A9 expression either.In summary, in the first part of this thesis, we showed that treatment with Q compounds can reduce recruitment of monocytes to the site of inflammation. Given the important role of these cells in promoting the development of inflammatory diseases and cancer, this observation may partially explain the ameliorating effects of the Q compounds in a broad range of disease models. Furthermore, the second part of the thesis shows the induction of formation of S100A9 homodimer in vivo under inflammatory conditions and cancer, which may create a positive feedback loop for the propagation of inflammatory cascades. Our results also suggest that there is a requirement for a complex interplay of different factors in vivo for induction of S100A9 expression.
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| 24. |
- Tahvili, Sahar, et al.
(author)
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Solving complex maintenance planning optimization problems using stochastic simulation and multi-criteria fuzzy decision making
- 2014
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In: 10TH INTERNATIONAL CONFERENCE ON MATHEMATICAL PROBLEMS IN ENGINEERING, AEROSPACE AND SCIENCES: ICNPAA 2014 Conference date: 15–18 July 2014 Location: Narvik, Norway ISBN: 978-0-7354-1276-7 Editor: Seenith Sivasundaram Volume number: 1637 Published: 10 december 2014. - : American Institute of Physics (AIP). - 9780735412767 ; , s. 766-775
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Conference paper (peer-reviewed)abstract
- One of the most important factors in the operations of many cooperations today is to maximize profit and one important tool to that effect is the optimization of maintenance activities. Maintenance activities is at the largest level divided into two major areas, corrective maintenance (CM) and preventive maintenance (PM). When optimizing maintenance activities, by a maintenance plan or policy, we seek to find the best activities to perform at each point in time, be it PM or CM. We explore the use of stochastic simulation, genetic algorithms and other tools for solving complex maintenance planning optimization problems in terms of a suggested framework model based on discrete event simulation.
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| 25. |
- Tahvili, Sahar, et al.
(author)
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SOrTES : A Supportive Tool for Stochastic Scheduling of Manual Integration Test Cases
- 2019
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In: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 7, s. 12928-12946
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Journal article (peer-reviewed)abstract
- The main goal of software testing is to detect as many hidden bugs as possible in the final software product before release. Generally, a software product is tested by executing a set of test cases, which can be performed manually or automatically. The number of test cases which are required to test a software product depends on several parameters such as the product type, size, and complexity. Executing all test cases with no particular order can lead to waste of time and resources. Test optimization can provide a partial solution for saving time and resources which can lead to the final software product being released earlier. In this regard, test case selection, prioritization, and scheduling can be considered as possible solutions for test optimization. Most of the companies do not provide direct support for ranking test cases on their own servers. In this paper, we introduce, apply, and evaluate sOrTES as our decision support system for manual integration of test scheduling. sOrTES is a Python-based supportive tool which schedules manual integration test cases which are written in a natural language text. The feasibility of sOrTES is studied by an empirical evaluation which has been performed on a railway use-case at Bombardier Transportation, Sweden. The empirical evaluation indicates that around 40 % of testing failure can be avoided by using the proposed execution schedules by sOrTES, which leads to an increase in the requirements coverage of up to 9.6%.
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| 26. |
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| 27. |
- Tahvili, Sahar, et al.
(author)
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Strategic maintenance planning by fuzzy AHP and Markov Decision Processes
- 2015
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In: ASMDA 2015 Proceedings. - : ISAST: International Society for the Advancement of Science and Technology. - 9786185180058 ; , s. 991-1004, s. 297-310
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Conference paper (peer-reviewed)abstract
- The work of engineering and business professionals includes making a series of decisions and optimizations. Real world decision making problems faced by decision makers (DM) involve multiple, usually conflicting, criteria. These multicriteria decision making problems (MCDM) are usually complicated and large in scale. In strategic Maintenance planning, choices are made on where to focus time and effort, where to spend money. We consider a framework for strategic maintenance planning in a modern maintenance driven organization. Our focus is on a multi-stage framework in which the planning is divided into two stages, identifying an optimal set of possible actions and finding the optimal decision policy for these actions for each point in time as a function of the stochastically evolving system state. To this respect we consider the MCDM method of AHP (Analytical hierarchical programming) in a fuzzy environment, and Markov decision processes (MDP).
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| 28. |
- Tahvili, Sahar, et al.
(author)
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Test Case Prioritization Using Multi Criteria Decision Making Methods
- 2016. - 4
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In: Danish Society for Operations Research. ; 26, s. 9-11
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Journal article (peer-reviewed)abstract
- The lack of a systematic approach to decision making might leads to a non-optimal usage of resources. Nowadays, the real world decision making problems are multiple criteria, complex, large scale and generally consist of uncertainty and vagueness. Multiple-criteria decision making (MCDM) is a subset of operations research and is divided into Multi-Objective Decision Making (MODM) and Multi-Attribute Decision Making (MADM). The principal objective of the present article is proposing a systematic multi-criteria design making approach in the area of software testing that will be exemplified by an industrial example.
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| 29. |
- Tahvili, Sahar, et al.
(author)
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Towards Earlier Fault Detection by Value-Driven Prioritization of Test Cases Using Fuzzy TOPSIS
- 2016. - 9
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In: Information Technology: New Generations. - Cham : Springer International Publishing. - 9783319324661 - 9783319324678 ; , s. 745-759
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Conference paper (peer-reviewed)abstract
- In industrial software testing, development projects typically set up and maintain test suites containing large numbers of test cases. Executing a large number of test cases can be expensive in terms of effort and wall-clock time. Moreover, indiscriminate execution of all available test cases typically lead to sub-optimal use of testing resources. On the other hand, selecting too few test cases for execution might leave a large number of faults undiscovered. Limiting factors such as allocated budget and time constraints for testing further emphasizes the importance of test case prioritization in order to identify test cases that enable earlier detection of faults while respecting such constraints. In this paper, we propose a multi-criteria decision making approach for prioritizing test cases in order to detect faults earlier. This is achieved by applying the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) decision making technique combined with fuzzy principles. Our solution is based on important criteria such as fault detection probability, execution time, complexity, and other test case properties. By applying the approach on a train control management subsystem from Bombardier Transportation in Sweden, we demonstrate how it helps, in a systematic way, to identify test cases that can lead to early detection of faults while respecting various criteria.
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| 30. |
- Tahvili, Sahar, et al.
(author)
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Towards Execution Time Prediction for Test Cases from Test Specification
- 2017
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In: 2017 43RD EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA). - Vienna, Austria. - 9781538621417 ; , s. 421-425
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Conference paper (peer-reviewed)abstract
- Knowing the execution time of test cases is important to perform test scheduling, prioritization and progress monitoring. This short paper presents a novel approach for predicting the execution time of test cases based on test specifications and available historical data on previously executed test cases. Our approach works by extracting timing information (measured and maximum execution time) for various steps in manual test cases. This information is then used to estimate the maximum time for test steps that have not previously been executed, but for which textual specifications exist. As part of our approach natural language parsing of the specifications is performed to identify word combinations to check whether existing timing information on various test activities already exists or not. Finally, linear regression is used to predict the actual execution time for test cases. A proof-of-concept use-case at Bombardier transportation serves to evaluate the proposed approach.
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