| 1. |
- Abbas, Muhammad, et al.
(författare)
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Automated Reuse Recommendation of Product Line Assets Based on Natural Language Requirements
- 2020
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Ingår i: Lecture Notes in Computer Science. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030646936 ; , s. 173-189, s. 173-189, s. 173-189
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Konferensbidrag (refereegranskat)abstract
- Software product lines (SPLs) are based on reuse rationale to aid quick and quality delivery of complex products at scale. Deriving a new product from a product line requires reuse analysis to avoid redundancy and support a high degree of assets reuse. In this paper, we propose and evaluate automated support for recommending SPL assets that can be reused to realize new customer requirements. Using the existing customer requirements as input, the approach applies natural language processing and clustering to generate reuse recommendations for unseen customer requirements in new projects. The approach is evaluated both quantitatively and qualitatively in the railway industry. Results show that our approach can recommend reuse with 74% accuracy and 57.4% exact match. The evaluation further indicates that the recommendations are relevant to engineers and can support the product derivation and feasibility analysis phase of the projects. The results encourage further study on automated reuse analysis on other levels of abstractions.
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| 2. |
- Abbas, Muhammad, et al.
(författare)
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Is Requirements Similarity a Good Proxy for Software Similarity? : An Empirical Investigation in Industry
- 2021
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Ingår i: <em>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) </em>27th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2021, 12 April 2021 - 15 April 2021. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030731274 ; , s. 3-18, s. 3-18
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Konferensbidrag (refereegranskat)abstract
- [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.
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| 3. |
- Abbas, Muhammad, et al.
(författare)
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Keywords-based test categorization for Extra-Functional Properties
- 2020
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Ingår i: 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). - : IEEE. - 9781728110752 ; , s. 153-156
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Konferensbidrag (refereegranskat)abstract
- Categorizing existing test specifications can provide insights on coverage of the test suite to extra-functional properties. Manual approaches for test categorization can be time-consuming and prone to error. In this short paper, we propose a semi-automated approach for semantic keywords-based textual test categorization for extra-functional properties. The approach is the first step towards coverage-based test case selection based on extra-functional properties. We report a preliminary evaluation of industrial data for test categorization for safety aspects. Results show that keyword-based approaches can be used to categorize tests for extra-functional properties and can be improved by considering contextual information of keywords.
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| 4. |
- Abbas, Muhammad, et al.
(författare)
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Making Sense of Failure Logs in an Industrial DevOps Environment
- 2023
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Ingår i: Advances in Intelligent Systems and Computing book series (AISC,volume 1445). - : Springer International Publishing. ; , s. 217-226
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Konferensbidrag (refereegranskat)abstract
- Processing and reviewing nightly test execution failure logs for large industrial systems is a tedious activity. Furthermore, multiple failures might share one root/common cause during test execution sessions, and the review might therefore require redundant efforts. This paper presents the LogGrouper approach for automated grouping of failure logs to aid root/common cause analysis and for enabling the processing of each log group as a batch. LogGrouper uses state-of-art natural language processing and clustering approaches to achieve meaningful log grouping. The approach is evaluated in an industrial setting in both a qualitative and quantitative manner. Results show that LogGrouper produces good quality groupings in terms of our two evaluation metrics (Silhouette Coefficient and Calinski-Harabasz Index) for clustering quality. The qualitative evaluation shows that experts perceive the groups as useful, and the groups are seen as an initial pointer for root cause analysis and failure assignment.
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| 5. |
- Abbas, Muhammad, et al.
(författare)
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On the relationship between similar requirements and similar software : A case study in the railway domain
- 2023
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Ingår i: Requirements Engineering. - : Springer Science and Business Media Deutschland GmbH. - 0947-3602 .- 1432-010X. ; 28, s. 23-47
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Tidskriftsartikel (refereegranskat)abstract
- Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspect comes into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. 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 change impact analysis.
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| 6. |
- Abbas, Muhammad, et al.
(författare)
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Product line adoption in industry : an experience report from the railway domain
- 2020
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Ingår i: SPLC '20: Proceedings of the 24th ACM Conference on Systems and Software Product Line: Volume A. - New York, NY, USA : Association for Computing Machinery. - 9781450375696 ; , s. 130-141, s. 130-141
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Konferensbidrag (refereegranskat)abstract
- The software system controlling a train is typically deployed on various hardware architectures and must process various signals across those deployments. The increase of such customization scenarios and the needed adherence of the software to various safety standards in different application domains has led to the adoption of product line engineering within the railway domain. This paper explores the current state-of-practice of software product line development within a team developing industrial embedded software for a train propulsion control system. Evidence is collected using a focus group session with several engineers and through inspection of archival data. We report several benefits and challenges experienced during product line adoption and deployment. Furthermore, we identify and discuss improvement opportunities, focusing mainly on product line evolution and test automation.
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| 7. |
- Abbas, Muhammad, et al.
(författare)
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Requirements-Driven Reuse Recommendation
- 2021
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Ingår i: Proceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A. - New York, NY, USA : Association for Computing Machinery.
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Konferensbidrag (refereegranskat)abstract
- This tutorial explores requirements-based reuse recommendation for product line assets in the context of clone-and-own product lines.
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| 8. |
- Abbas, Muhammad
(författare)
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Requirements-Level Reuse Recommendation and Prioritization of Product Line Assets
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Software systems often target a variety of different market segments. Targeting varying customer requirements requires a product-focused development process. Software Product Line (SPL) engineering is one possible approach based on reuse rationale to aid quick delivery of quality product variants at scale. SPLs reuse common features across derived products while still providing varying configuration options. The common features, in most cases, are realized by reusable assets. In practice, the assets are reused in a clone-and-own manner to reduce the upfront cost of systematic reuse. Besides, the assets are implemented in increments, and requirements prioritization also has to be done. In this context, the manual reuse analysis and prioritization process become impractical when the number of derived products grows. Besides, the manual reuse analysis process is time-consuming and heavily dependent on the experience of engineers.In this licentiate thesis, we study requirements-level reuse recommendation and prioritization for SPL assets in industrial settings. We first identify challenges and opportunities in SPLs where reuse is done in a clone-and-own manner. We then focus on one of the identified challenges: requirements-based SPL assets reuse and provide automated support for identifying reuse opportunities for SPL assets based on requirements. Finally, we provide automated support for requirements prioritization in the presence of dependencies resulting from reuse.
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| 9. |
- Bashir, Sarmad, et al.
(författare)
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Requirement or Not, That is the Question : A Case from the Railway Industry
- 2023
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Ingår i: <em>Lecture Notes in Computer Science. </em>Volume 13975. Pages 105 - 121 2023. - : Springer Science and Business Media Deutschland GmbH. - 9783031297854 ; , s. 105-121
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Konferensbidrag (refereegranskat)abstract
- 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. 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. 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. 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. © 2023, The Author(s)
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| 10. |
- Bashir, Sarmad, et al.
(författare)
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Requirements Classification for Smart Allocation : A Case Study in the Railway Industry
- 2023
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Ingår i: 31st IEEE International Requirements Engineering Conference. - Hannover, Germany : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Allocation of requirements to different teams is a typical preliminary task in large-scale system development projects. This critical activity is often performed manually and can benefit from automated requirements classification techniques. To date, limited evidence is available about the effectiveness of existing machine learning (ML) approaches for requirements classification in industrial cases. This paper aims to fill this gap by evaluating state-of-the-art language models and ML algorithms for classification in the railway industry. Since the interpretation of the results of ML systems is particularly relevant in the studied context, we also provide an information augmentation approach to complement the output of the ML-based classification. Our results show that the BERT uncased language model with the softmax classifier can allocate the requirements to different teams with a 76% F1 score when considering requirements allocation to the most frequent teams. Information augmentation provides potentially useful indications in 76% of the cases. The results confirm that currently available techniques can be applied to real-world cases, thus enabling the first step for technology transfer of automated requirements classification. The study can be useful to practitioners operating in requirements-centered contexts such as railways, where accurate requirements classification becomes crucial for better allocation of requirements to various teams.
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