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Saadatmand, Mehrdad, PhDORCID iD iconorcid.org/0000-0002-1512-0844
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Publications (10 of 57) Show all publications
Bashir, S., Abbas, M., Saadatmand, M., Enoiu, E. P., Bohlin, M. & Lindberg, P. (2023). Requirement or Not, That is the Question: A Case from the Railway Industry. In: Lecture Notes In Computer Science: . Paper presented at 29th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2023, Barcelona, Spain, 17-20 April, 2023 (pp. 105-121). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Requirement or Not, That is the Question: A Case from the Railway Industry
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2023 (English)In: Lecture Notes In Computer Science, Springer Science and Business Media Deutschland GmbH , 2023, p. 105-121Conference paper, Published paper (Refereed)
Abstract [en]

[Context and Motivation] Requirements in tender documents are often mixed with other supporting information. Identifying requirements in large tender documents could aid the bidding process and help estimate the risk associated with the project. [Question/problem] Manual identification of requirements in large documents is a resource-intensive activity that is prone to human error and limits scalability. This study compares various state-of-the-art approaches for requirements identification in an industrial context. For generalizability, we also present an evaluation on a real-world public dataset. [Principal ideas/results] We formulate the requirement identification problem as a binary text classification problem. Various state-of-the-art classifiers based on traditional machine learning, deep learning, and few-shot learning are evaluated for requirements identification based on accuracy, precision, recall, and F1 score. Results from the evaluation show that the transformer-based BERT classifier performs the best, with an average F1 score of 0.82 and 0.87 on industrial and public datasets, respectively. Our results also confirm that few-shot classifiers can achieve comparable results with an average F1 score of 0.76 on significantly lower samples, i.e., only 20% of the data. [Contribution] There is little empirical evidence on the use of large language models and few-shots classifiers for requirements identification. This paper fills this gap by presenting an industrial empirical evaluation of the state-of-the-art approaches for requirements identification in large tender documents. We also provide a running tool and a replication package for further experimentation to support future research in this area.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 13975 LNCS
Keywords
NLP, Requirements classification, Requirements identification, tender documents, Deep learning, Information retrieval systems, Natural language processing systems, Requirements engineering, Risk perception, Text processing, Bidding process, F1 scores, Human errors, Manual identification, Public dataset, Railway industry, Requirement identification, Requirements classifications, State-of-the-art approach, Classification (of information)
National Category
Software Engineering
Identifiers
urn:nbn:se:mdh:diva-62331 (URN)10.1007/978-3-031-29786-1_8 (DOI)2-s2.0-85152587069 (Scopus ID)9783031297854 (ISBN)
Conference
29th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2023, Barcelona, Spain, 17-20 April, 2023
Available from: 2023-04-26 Created: 2023-04-26 Last updated: 2023-04-26Bibliographically approved
Helali Moghadam, M., Saadatmand, M., Borg, M., Bohlin, M. & Lisper, B. (2022). An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning. Software quality journal, 127-159
Open this publication in new window or tab >>An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning
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2022 (English)In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, p. 127-159Article in journal (Refereed) Published
Abstract [en]

Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points. Current approaches to tackle automated generation of performance test cases mainly involve using source code or system model analysis or use-case based techniques. However, source code and system models might not always be available at testing time. On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learnt by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learnt policy could later be reused for similar software systems under test, thus leading to higher test efficiency. We propose SaFReL, a self-adaptive fuzzy reinforcement learning-based performance testing framework. SaFReL learns the optimal policy to generate performance test cases through an initial learning phase, then reuses it during a transfer learning phase, while keeping the learning running and updating the policy in the long term. Through multiple experiments on a simulated environment, we demonstrate that our approach generates the target performance test cases for different programs more efficiently than a typical testing process, and performs adaptively without access to source code and performance models.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Performance testing, Stress testing, Test case generation, Reinforcement learning, Autonomous testing
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-47471 (URN)10.1007/s11219-020-09532-z (DOI)000627215600001 ()2-s2.0-85102446552 (Scopus ID)
Available from: 2020-04-06 Created: 2020-04-06 Last updated: 2023-09-13Bibliographically approved
Helali Moghadam, M., Borg, M., Saadatmand, M., Mousavirad, S. J., Bohlin, M. & Lisper, B. (2022). Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing.
Open this publication in new window or tab >>Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing
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2022 (English)Report (Other academic)
Abstract [en]

This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (μ+ λ) and (μ,λ) evolution strategies(ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints.

Publisher
p. 20
Keywords
Machine Learning Testing, Search-Based Testing, Evolutionary Computation, Advanced Driver Assistance Systems, Deep Learning, Lane-Keeping System
National Category
Computer Sciences Software Engineering Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-57607 (URN)10.48550/arXiv.2203.12026 (DOI)
Available from: 2022-03-12 Created: 2022-03-12 Last updated: 2023-09-13Bibliographically approved
Saadatmand, M., Truscan, D. & Enoiu, E. P. (2022). Message from the ITEQS 2022 Workshop Chairs. Paper presented at 4 April 2022 through 13 April 2022. 14th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2022
Open this publication in new window or tab >>Message from the ITEQS 2022 Workshop Chairs
2022 (English)In: 14th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2022Article in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2022
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-59582 (URN)10.1109/ICSTW55395.2022.00006 (DOI)2-s2.0-85133233202 (Scopus ID)
Conference
4 April 2022 through 13 April 2022
Note

Conference code: 179921; Export Date: 13 July 2022; Editorial

Available from: 2022-07-13 Created: 2022-07-13 Last updated: 2022-07-13Bibliographically approved
Bucaioni, A., Di Silvestro, F., Singh, I., Saadatmand, M. & Muccini, H. (2022). Model‐based generation of test scripts across product variants: An experience report from the railway industry. Journal of Software: Evolution and Process, 34(11), Article ID e2498.
Open this publication in new window or tab >>Model‐based generation of test scripts across product variants: An experience report from the railway industry
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2022 (English)In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 34, no 11, article id e2498Article in journal (Refereed) Published
Abstract [en]

Software product line engineering emerged as an effective approach for the development of families of software-intensive systems in several industries. Although its use has been widely discussed and researched, there are still several open challenges for its industrial adoption and application. One of these is how to efficiently develop and reuse shared software artifacts, which have dependencies on the underlying electrical and hardware systems of products in a family. In this work, we report on our experience in tackling such a challenge in the railway industry and present a model-based approach for the automatic generation of test scripts for product variants in software product lines. The proposed approach is the result of an effort leveraging the experiences and results from the technology transfer activities with our industrial partner Alstom SA in Sweden. We applied and evaluated the proposed approach on the Aventra software product line from Alstom SA. The evaluation showed that the proposed approach mitigates the development effort, development time, and consistency drawbacks associated with the traditional, manual creation of test scripts. We performed an online survey involving 37 engineers from Alstom SA for collecting feedback on the approach. The result of the survey further confirms the aforementioned benefits.

Keywords
automation, model-based software engineering, product line engineering, testing
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-60683 (URN)10.1002/smr.2498 (DOI)000836874900001 ()2-s2.0-85135404407 (Scopus ID)
Funder
Vinnova, PANORAMAVinnova, XIVT
Available from: 2022-11-21 Created: 2022-11-21 Last updated: 2023-09-15Bibliographically approved
Mousavirad, S. J., Helali Moghadam, M., Saadatmand, M., Chakrabortty, R., Schaefer, G. & Oliva, D. (2022). RWS-L-SHADE: An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation. In: Lecture Notes in Computer Science, vol. 13324: . Paper presented at 25th European Conference on the Applications of Evolutionary Computation, EvoApplications 2022, Madrid, Spain, 20/4-22/4, 2022 (pp. 255-268). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>RWS-L-SHADE: An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation
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2022 (English)In: Lecture Notes in Computer Science, vol. 13324, Springer Science and Business Media Deutschland GmbH , 2022, p. 255-268Conference paper, Published paper (Refereed)
Abstract [en]

Differential evolution (DE) is widely used for global optimisation problems due to its simplicity and efficiency. L-SHADE is a state-of-the-art variant of DE algorithm that incorporates external archive, success-history-based parameter adaptation, and linear population size reduction. L-SHADE uses a current-to-pbest/1/bin strategy for mutation operator, while all individuals have the same probability to be selected. In this paper, we propose a novel L-SHADE algorithm, RWS-L-SHADE, based on a roulette wheel selection strategy so that better individuals have a higher priority and worse individuals are less likely to be selected. Our extensive experiments on the CEC-2017 benchmark functions and dimensionalities of 30, 50 and 100 indicate that RWS-L-SHADE outperforms L-SHADE. 

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2022
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 13224 LNCS
Keywords
CEC-2017 benchmark functions, Differential evolution, L-SHADE algorithm, Optimisation, Roulette wheel selection strategy, Global optimization, Wheels, Benchmark functions, CEC-2017 benchmark function, Differential evolution algorithms, Global optimization problems, Numerical optimizations, Optimisations, Roulette-wheel selections, State of the art, Population statistics
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:mdh:diva-58243 (URN)10.1007/978-3-031-02462-7_17 (DOI)000873604200017 ()2-s2.0-85129308303 (Scopus ID)9783031024610 (ISBN)
Conference
25th European Conference on the Applications of Evolutionary Computation, EvoApplications 2022, Madrid, Spain, 20/4-22/4, 2022
Available from: 2022-05-18 Created: 2022-05-18 Last updated: 2022-11-17Bibliographically approved
Moravvej, S. V., Mousavirad, S. J., Helali Moghadam, M. & Saadatmand, M. (2021). An LSTM-Based Plagiarism Detection via Attention Mechanism and a Population-Based Approach for Pre-training Parameters with Imbalanced Classes. In: Lect. Notes Comput. Sci.: . Paper presented at 28th International Conference on Neural Information Processing, ICONIP 2021 (pp. 690-701). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>An LSTM-Based Plagiarism Detection via Attention Mechanism and a Population-Based Approach for Pre-training Parameters with Imbalanced Classes
2021 (English)In: Lect. Notes Comput. Sci., Springer Science and Business Media Deutschland GmbH , 2021, p. 690-701Conference paper, Published paper (Refereed)
Abstract [en]

Plagiarism is one of the leading problems in academic and industrial environments, which its goal is to find the similar items in a typical document or source code. This paper proposes an architecture based on a Long Short-Term Memory (LSTM) and attention mechanism called LSTM-AM-ABC boosted by a population-based approach for parameter initialization. Gradient-based optimization algorithms such as back-propagation (BP) are widely used in the literature for learning process in LSTM, attention mechanism, and feed-forward neural network, while they suffer from some problems such as getting stuck in local optima. To tackle this problem, population-based metaheuristic (PBMH) algorithms can be used. To this end, this paper employs a PBMH algorithm, artificial bee colony (ABC), to moderate the problem. Our proposed algorithm can find the initial values for model learning in all LSTM, attention mechanism, and feed-forward neural network, simultaneously. In other words, ABC algorithm finds a promising point for starting BP algorithm. For evaluation, we compare our proposed algorithm with both conventional and population-based methods. The results clearly show that the proposed method can provide competitive performance.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2021
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 13110 LNCS
Keywords
Artificial bee colony, Attention mechanism, Back-propagation, LSTM, Plagiarism, Feedforward neural networks, Intellectual property, Learning algorithms, Optimization, Academic environment, Attention mechanisms, Back Propagation, Feed forward neural net works, Imbalanced class, Industrial environments, Meta-heuristics algorithms, Plagiarism detection, Pre-training, Training parameters, Long short-term memory
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-56879 (URN)10.1007/978-3-030-92238-2_57 (DOI)2-s2.0-85121899875 (Scopus ID)9783030922375 (ISBN)
Conference
28th International Conference on Neural Information Processing, ICONIP 2021
Available from: 2022-01-12 Created: 2022-01-12 Last updated: 2022-03-14Bibliographically approved
Mousavirad, S. J., Schaefer, G., Korovin, I., Oliva, D., Helali Moghadam, M. & Saadatmand, M. (2021). HMS-OS: Improving the Human Mental Search Optimisation Algorithm by Grouping in both Search and Objective Space. In: 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings: . Paper presented at 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021, 5 December 2021 through 7 December 2021. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>HMS-OS: Improving the Human Mental Search Optimisation Algorithm by Grouping in both Search and Objective Space
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2021 (English)In: 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2021Conference paper, Published paper (Refereed)
Abstract [en]

The human mental search (HMS) algorithm is a relatively recent population-based metaheuristic algorithm, which has shown competitive performance in solving complex optimisation problems. It is based on three main operators: mental search, grouping, and movement. In the original HMS algorithm, a clustering algorithm is used to group the current population in order to identify a promising region in search space, while candidate solutions then move towards the best candidate solution in the promising region. In this paper, we propose a novel HMS algorithm, HMS-OS, which is based on clustering in both objective and search space, where clustering in objective space finds a set of best candidate solutions whose centroid is then also used in updating the population. For further improvement, HMS-OS benefits from an adaptive selection of the number of mental processes in the mental search operator. Experimental results on CEC-2017 benchmark functions with dimensionalities of 50 and 100, and in comparison to other optimisation algorithms, indicate that HMS-OS yields excellent performance, superior to those of other methods.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2021
Keywords
Clustering, Human mental search, Metaheuristics, Objective space, Optimisation, Benchmarking, Clustering algorithms, Clusterings, Competitive performance, Complex optimization problems, Meta-heuristics algorithms, Metaheuristic, Optimisations, Search Algorithms, Search spaces, Optimization
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-58807 (URN)10.1109/SSCI50451.2021.9660101 (DOI)000824464300278 ()2-s2.0-85120029697 (Scopus ID)9781728190488 (ISBN)
Conference
2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021, 5 December 2021 through 7 December 2021
Note

Conference code: 176593; Cited By :1; Export Date: 8 June 2022; Conference Paper; Funding text 1: This research is funded within the SFEDU development program (PRIORITY 2030).

Available from: 2022-07-13 Created: 2022-07-13 Last updated: 2022-08-03Bibliographically approved
Abbas, M., Ferrari, A., Shatnawi, A., Enoiu, E. P. & Saadatmand, M. (2021). Is Requirements Similarity a Good Proxy for Software Similarity? An Empirical Investigation in Industry. In: Dalpiaz, Fabiano and Spoletini, Paola (Eds.) (Ed.), Requirements Engineering: Foundation for Software Quality: . Paper presented at The 27th International Working Conference on Requirements Engineering: Foundation for Software Quality (pp. 3-18). Cham: Springer International Publishing, 12685
Open this publication in new window or tab >>Is Requirements Similarity a Good Proxy for Software Similarity? An Empirical Investigation in Industry
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2021 (English)In: Requirements Engineering: Foundation for Software Quality / [ed] Dalpiaz, Fabiano and Spoletini, Paola (Eds.), Cham: Springer International Publishing , 2021, Vol. 12685, p. 3-18Conference paper, Published paper (Refereed)
Abstract [en]

[Context and Motivation] Content-based recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a new requirement is proposed by a stakeholder, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. [Question/problem] Several NLP approaches for similarity computation are available, and there is little empirical evidence on the adoption of an effective technique in recommender systems specifically oriented to requirements-based code reuse. [Principal ideas/results] This study compares different state-of-the-art NLP approaches and correlates the similarity among requirements with the similarity of their source code. The evaluation is conducted on real-world requirements from two industrial projects in the railway domain. Results show that requirements similarity computed with the traditional tf-idf approach has the highest correlation with the actual software similarity in the considered context. Furthermore, results indicate a moderate positive correlation with Spearman's rank correlation coefficient of more than 0.5. [Contribution] Our work is among the first ones to explore the relationship between requirements similarity and software similarity. In addition, we also identify a suitable approach for computing requirements similarity that reflects software similarity well in an industrial context. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and categorization.

Place, publisher, year, edition, pages
Cham: Springer International Publishing, 2021
Series
Lecture Notes in Computer Science, ISSN 0302-9743
Keywords
Requirements similarity, Software similarity, Correlation
National Category
Engineering and Technology
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-53514 (URN)10.1007/978-3-030-73128-1_1 (DOI)000788007000001 ()2-s2.0-85107415615 (Scopus ID)978-3-030-73128-1 (ISBN)
Conference
The 27th International Working Conference on Requirements Engineering: Foundation for Software Quality
Available from: 2021-02-23 Created: 2021-02-23 Last updated: 2022-06-07Bibliographically approved
Bucaioni, A., Di Silvestro, F., Singh, I., Saadatmand, M., Muccini, H. & Jochumsson, T. (2021). Model-based Automation of Test Script Generation Across Product Variants: a Railway Perspective. In: 2nd ACM/IEEE International Conference on Automation of Software Test AST 2021: . Paper presented at 2nd ACM/IEEE International Conference on Automation of Software Test AST 2021, 28 May 2021, Virtual, Sweden (pp. 20-29).
Open this publication in new window or tab >>Model-based Automation of Test Script Generation Across Product Variants: a Railway Perspective
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2021 (English)In: 2nd ACM/IEEE International Conference on Automation of Software Test AST 2021, 2021, p. 20-29Conference paper, Published paper (Refereed)
Abstract [en]

In this work, we report on our experience indefining and applying a model-based approach for the automaticgeneration of test scripts for product variants in software productlines. The proposed approach is the result of an effort leveragingthe experiences and results from the technology transfer activitieswith our industrial partner Bombardier Transportation. Theproposed approach employs metamodelling and model transfor-mations for representing different testing artefacts and makingtheir generation automatic. We demonstrate the industrial ap-plicability and efficiency of the proposed approach using theBombardier Transportation Aventra software product line. Weobserve that the proposed approach mitigates the developmenteffort, time consumption and consistency drawbacks typical oftraditional strategies.

National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-54304 (URN)10.1109/AST52587.2021.00011 (DOI)000695707800003 ()2-s2.0-85113803656 (Scopus ID)978-1-6654-3567-3 (ISBN)
Conference
2nd ACM/IEEE International Conference on Automation of Software Test AST 2021, 28 May 2021, Virtual, Sweden
Projects
A-CPS - Automation in High-performance Cyber Physical Systems DevelopmentPANORAMA - Boosting Design Efficiency for Heterogeneous³ Systems
Available from: 2021-06-01 Created: 2021-06-01 Last updated: 2021-10-07Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-1512-0844

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