Types of Contributions
Special issues aim to publish four types of articles:
- Survey papers(Tutorials)
- Contributed papers (Detailed expositions of new research or applications.)
- Short papers(Ideas only)
- software reviews(Case Studies)
Theme of Session:
The main aim of this special session is to introduce state-of-the-art and advanced techniques for developing software systems dealing with the machine and deep learning. The objective of this special issue to resolve the difficult task for effort estimation and reusability prediction. Although several techniques proposed by the different authors but required more intelligent techniques to predict the reusability estimation from the software code. Predicting the reusability metrics is one of the challenging tasks in software engineering domain. According to the recent studies, soft computing has become an important issue in the development of software engineering field. Soft Computing uses special techniques like neural networks, fuzzy logic, Support Vector Machines, Evolutionary computation to minimize the execution periods of functional tasks. The aim of this special issue is to highlight the utility of different soft computing methodologies on software engineering problems. For a long time, Software Engineering has served as a bridge between other disciplines and their information needs. Its application domain has broadened and evolved day by day, and also the complexity of the engaged problems has increased.
Software Reusability Metrics plays a vital role in the software industry. Estimation of software metrics are the tedious task now a days. The objective of this special issue to resolve the difficult task for effort estimation and reusability prediction. Although several techniques proposed by the different authors but required more intelligent techniques to predict the reusability estimation from the software code. Predicting the reusability metrics is one of the challenging tasks in software engineering domain.
Software Reusability Cost Estimation is another challenging task in the industry. There are several approaches are proposed but still we are looking for the innovative models and algorithms which is able to predict the cost estimation of the software industry.
Software-intensive systems are becoming more and more complex and therefore, we need more intelligent approaches to solve several challenging problems in this domain. Soft computing has many applications in Software Engineering. Researchers applied computational intelligence techniques for many challenging problems such as software cost estimation, and software defect prediction so far. Since software engineering problems have many dimensions, we need to investigate the use of soft computing on these challenging problems.
Software defect prediction models predict the defect-prone modules based on previous version’s software metrics and the fault labels. After the training model is built, it’s used to predict the defect-proneness labels of the current modules. Defect-prone modules are tested in detail and therefore, the limited testing resources are utilized effectively. Soft Computing may solve some specific challenges in this domain as well.
Software vulnerability prediction approaches look like similar to defect prediction, but they focus on vulnerability-prone modules. With the wide-spread use of web technologies in industry, it became crucial to eliminate the security problems, and these models contribute to solve some of these challenges. Researchers used text mining and software metrics as features of the models. This is relatively more recent problem compared to the software defect prediction, and therefore, Soft Computing may contribute to solve some challenging problems.
Topics of Interest: We invite original (un-published) research contributions based on the above mentioned theme including following topics but not limited to:
- Mining software engineering repositories Software testing, verification, and bug localization
- Modeling, measuring, and assessing product Empirically-based decision making
- Experiments and quasi-experiments Industrial experience & Software project experience,
- Evaluation and comparison of techniques Deep learning and data analytics in software engineering
- Secure software engineering Fuzzy Logic and Fuzzy Systems
- Evolutionary Computation
- Evolutionary Algorithms
- Genetic Algorithms
- Differential Evolution
- Metaheuristic and Swarm Intelligence
- Ant Colony Optimization Particle Swarm optimization
- Probabilistic Reasoning
- Bayesian Networks
- Vision for software engineering education in the future Methodological aspects of software engineering education
- Domain engineering Software engineering education for Cloud Computing
- Development and evaluation of empirical prediction systems or software estimation models
- Open source in software engineering education
Paper Submission Process:
Please submit your paper (in word/pdf format) at
Program Committee: Name(s), Affiliation(s)
For any further queries related to this special session, please contact the session chairs at:
E-mail ID: firstname.lastname@example.org
Mobile No.: +91-7978557037/+91-9437771064