Evaluation of estimation in software development using deep learning-modified neural network

Sreekanth, N.; Devi, Johana; Shukla, Amogh; Mohanty, Dipana Jyoti; Srinivas, Amedapu; Rao, G. N. V. Ramana; Alam, Afaque; Gupta, Ankur

doi:10.1007/s13204-021-02204-9

Cited by 21 publications

(2 citation statements)

References 28 publications

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“…This enhanced approach yields a promising 70% probability of successfully recommending software estimation techniques, providing a strategic tool for managing the complexities of Web application development [19]. Besides the recurrent neural networks underpinning FCM models, one study proposed a hybrid approach combining particle swarm optimization with a deep learning model to improve the evaluation metrics of the approach [20]. Furthermore, an artificial bee colony guided analogy-based estimation (BABE) model was introduced, which combines the artificial bee colony (ABC) algorithm with analogy-based estimation (ABE) for more accurate estimations.…”

Section: Related Workmentioning

confidence: 99%

Delving into Human Factors through LSTM by Navigating Environmental Complexity Factors within Use Case Points for Digital Enterprises

Rankovic,

Rankovic

2024

JTAER

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Meeting customer requirements in software project management, even for large digital enterprises, proves challenging due to unpredictable human factors. It involves meticulous planning and environmental factor analysis, ultimately benefiting both companies and customers. This paper came as a natural extension of our previous work where we left ourselves curious about what impact environmental complexity factors (ECFs) have in a use case point (UCP) approach. Additionally, we wanted to possibly decrease the mean magnitude relative error (MMRE) with deep learning models such as long-short-term-memory (LSTM) and gradient recurrent unit (GRU). The data augmentation technique was used to artificially increase the number of projects, since in the industry world, digital enterprises are not keen to share their data. The LSTM model outperformed the GRU and XGBoost models, while the average MMRE in all phases of the experiment for all models achieved 4.8%. Moreover, the post-agnostic models showed the overall and individual impact of eight ECFs, where the third ECF “team experience” on a new project has been shown as the most influential one. Finally, it is important to emphasize that effectively managing human factors within ECFs in UCPs can have a significant impact on the successful completion of a project.

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Section: Related Workmentioning

confidence: 99%

Delving into Human Factors through LSTM by Navigating Environmental Complexity Factors within Use Case Points for Digital Enterprises

Rankovic,

Rankovic

2024

JTAER

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“…Batool et al [37] proposed a model to predict software errors and defects by synthesizing not only deep learning but also various machine learning techniques. Sreekanth et al [38] improved current methodologies such as RE and MRE by evaluating the estimation required for software development. Bhuyan et al [39] demonstrated the superiority of the proposed feedforward neural network through a comparison between the feedforward neural network and the existing parametric SRGM.…”

Section: Related Researchmentioning

confidence: 99%

Deep-Learning Software Reliability Model Using SRGM as Activation Function

Kim,

Pham,

Chang

2023

Applied Sciences

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Software is widely used in various fields. There is no place where it is not used from the smallest part to the entire part. In particular, the tendency to rely on software is accelerating as the fields of artificial intelligence and big data become more widespread. Therefore, it is extremely important to evaluate the reliability of software because of the extensive damage that could occur if the software fails. Previously, software reliability models were developed based on mathematical and statistical grounds; however, immediate response was difficult. Therefore, in this study, a software reliability model was developed that depends on data using deep learning, and it was analyzed by replacing the activation function previously used in deep learning with the proposed software reliability model. Since the sigmoid function has a similar shape to the software reliability model, we utilized this to propose a deep learning software reliability model that replaces the activation function, the sigmoid function, with the software reliability function. Two datasets were compared and analyzed using 10 criteria, and the superiority of the proposed deep-learning software reliability model was proved. In addition, the results were compared by changing the parameters utilized in the proposed deep-learning software reliability model by −10%, −5%, 5%, and 10%, and it was found that the larger the parameters, the smaller the change.

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Einsatz des Self-Enforcing Networks für das Clustering von Pflichtenhefttexten in der Softwareentwicklung mithilfe von TF-IDF und Doc2Vec

Loy

2024

Neue Algorithmen Für Praktische Probleme

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Evaluation of estimation in software development using deep learning-modified neural network

Cited by 21 publications

References 28 publications

Delving into Human Factors through LSTM by Navigating Environmental Complexity Factors within Use Case Points for Digital Enterprises

Delving into Human Factors through LSTM by Navigating Environmental Complexity Factors within Use Case Points for Digital Enterprises

Deep-Learning Software Reliability Model Using SRGM as Activation Function

Einsatz des Self-Enforcing Networks für das Clustering von Pflichtenhefttexten in der Softwareentwicklung mithilfe von TF-IDF und Doc2Vec

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