| 1. |
- Allahviranloo, Tofigh, et al.
(författare)
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On the global solution of a fuzzy linear system
- 2014
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Ingår i: Journal of Fuzzy Set Valued Analysis. - : International Scientific Publications and Consulting Services. - 2193-4169. ; 2014
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)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.
(författare)
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Performance Analysis of Deep Anomaly Detection Algorithms for Commercial Microwave Link Attenuation
- 2020
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Ingår i: ICACSIS 2020. - : IEEE. ; , s. 47-52
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Konferensbidrag (refereegranskat)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.
(författare)
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Artificial Intelligence Techniques in System Testing
- 2023
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Ingår i: Optimising the software development process with artificial intelligence. - : Springer Science and Business Media Deutschland GmbH. - 9789811999475 - 9789811999482 ; , s. 221-240
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Bokkapitel (övrigt vetenskapligt/konstnärligt)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.
(författare)
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Induction of S100A9 homodimer formation in vivo
- 2018
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Ingår i: Biochemical and Biophysical Research Communications. - : Elsevier BV. - 0006-291X. ; , s. 564-568
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Tidskriftsartikel (refereegranskat)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.
(författare)
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A Dynamic Threshold Based Approach for Detecting the Test Limits
- 2021
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Ingår i: Sixteenth International Conference on Software Engineering Advances (ICSEA 2021). - : International Academy, Research, and Industry Association (IARIA). - 9781612088945 ; , s. 71-80
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Konferensbidrag (refereegranskat)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-
(författare)
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AI-Based Methods For Improved Testing of Radio Base Stations : A Case Study Towards Intelligent Manufacturing
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)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.
(författare)
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Cluster-Based Parallel Testing Using Semantic Analysis
- 2020
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Ingår i: 2020 IEEE International Conference On Artificial Intelligence Testing (AITest). - : IEEE. - 9781728169842 ; , s. 99-106
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Konferensbidrag (refereegranskat)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.
(författare)
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Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases
- 2020
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Ingår i: The Fifteenth International Conference on Software Engineering Advances. - : International Academy, Research and Industry Association (IARIA). - 9781612088273 ; , s. 90-97
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Konferensbidrag (refereegranskat)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.
(författare)
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A Fuzzy Decision Support Approach for Model-Based Tradeoff Analysis of Non-Functional Requirements
- 2015
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Ingår i: 12th International Conference on Information Technology. - Las Vegas, United States. - 9781479988273 ; , s. 112-121, s. 112-121
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Konferensbidrag (refereegranskat)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
(författare)
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A Decision Support System for Integration Test Selection
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)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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