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Algorithms
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Algorithms in Software Engineering
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Algorithms in Software Engineering

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 3698

Special Issue Editor

Dr. Piotr Kosiuczenko

E-Mail Website
Guest Editor
Institute of Information Systems, Faculty of Cybernetics, Military University of Technology, Sylwestra Kaliskiego 2, 00-908 Warszawa, Poland
Interests: software engineering; graphical modelling languages; contractual specification; logics; foundations of object-oriented languages; concurrent and real-time systems; data processing

Special Issue Information

Dear Colleagues,

The last two decades have seen significant advances in field of software engineering. This field includes diverse subfields such as system specification, modelling, implementation, testing, analysis, maintenance, software development methodologies, etc. Various methods and algorithms are used for those purposes. 

This Special Issue is devoted to algorithms used in software engineering and to the issues related to their development and application. We will publish papers that address the topic of algorithms both in terms of presenting new algorithms, and in how to develop and apply them in the contexts of model creation, evaluation and use, tool development, etc. We are also interested in papers devoted to algorithm engineering, i.e., their design, analysis, implementation, profiling, evaluation and application.

Dr. Piotr Kosiuczenko
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • software engineering
  • algorithms
  • algorithm engineering
  • model-checking algorithms
  • optimization algorithms
  • genetic algorithms
  • simulated annealing

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

27 pages, 3076 KiB  
Article
Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization
by Khadija Javed, Ren Shengbing, Muhammad Asim and Mudasir Ahmad Wani
Algorithms 2024, 17(5), 175; https://doi.org/10.3390/a17050175 - 24 Apr 2024
Viewed by 1465
Abstract
Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target [...] Read more.
Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance in the target project. To address these challenges, this research proposes Smote Correlation and Attention Gated recurrent unit based Long Short-Term Memory optimization (SCAG-LSTM), which first employs a novel hybrid technique that extends the synthetic minority over-sampling technique (SMOTE) with edited nearest neighbors (ENN) to rebalance class distributions and mitigate the issues caused by noisy and irrelevant instances in both source and target domains. Furthermore, correlation-based feature selection (CFS) with best-first search (BFS) is utilized to identify and select the most important features, aiming to reduce the differences in data distribution among projects. Additionally, SCAG-LSTM integrates bidirectional gated recurrent unit (Bi-GRU) and bidirectional long short-term memory (Bi-LSTM) networks to enhance the effectiveness of the long short-term memory (LSTM) model. These components efficiently capture semantic and contextual information as well as dependencies within the data, leading to more accurate predictions. Moreover, an attention mechanism is incorporated into the model to focus on key features, further improving prediction performance. Experiments are conducted on apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mylyn (AEEEM) and predictor models in software engineering (PROMISE) datasets and compared with active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), the two-phase feature importance amplification method (TFIA) on AEEEM and the two-phase transfer learning method (TPTL), domain adaptive kernel twin support vector machines method (DA-KTSVMO), and generative adversarial long-short term memory neural networks method (GB-CPDP) on PROMISE datasets. The results demonstrate that the proposed SCAG-LSTM model enhances the baseline models by 33.03%, 29.15% and 1.48% in terms of F1-measure and by 16.32%, 34.41% and 3.59% in terms of Area Under the Curve (AUC) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline models’ F1-measure by 42.60%, 32.00% and 25.10% and AUC by 34.90%, 27.80% and 12.96%. These findings suggest that the proposed model exhibits strong predictive performance. Full article
(This article belongs to the Special Issue Algorithms in Software Engineering)
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18 pages, 1954 KiB  
Article
Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking
by Yiming Fan and Meng Wang
Algorithms 2024, 17(1), 28; https://doi.org/10.3390/a17010028 - 10 Jan 2024
Viewed by 1501
Abstract
Software specifications are of great importance to improve the quality of software. To automatically mine specifications from software systems, some specification mining approaches based on finite-state automatons have been proposed. However, these approaches are inaccurate when dealing with large-scale systems. In order to [...] Read more.
Software specifications are of great importance to improve the quality of software. To automatically mine specifications from software systems, some specification mining approaches based on finite-state automatons have been proposed. However, these approaches are inaccurate when dealing with large-scale systems. In order to improve the accuracy of mined specifications, we propose a specification mining approach based on the ordering points to identify the clustering structure clustering algorithm and model checking. In the approach, the neural network model is first used to produce the feature values of states in the traces of the program. Then, according to the feature values, finite-state automatons are generated based on the ordering points to identify the clustering structure clustering algorithm. Further, the finite-state automaton with the highest F-measure is selected. To improve the quality of the finite-state automatons, we refine it based on model checking. The proposed approach was implemented in a tool named MCLSM and experiments, including 13 target classes, were conducted to evaluate its effectiveness. The experimental results show that the average F-measure of finite-state automatons generated by our method reaches 92.19%, which is higher than most related tools. Full article
(This article belongs to the Special Issue Algorithms in Software Engineering)
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