scholarly journals Hybrid Adaptive Software Development Capability: An Empirical Study

2014 ◽  
Vol 9 (10) ◽  
Author(s):  
Asif Qumer Gill
Keyword(s):  
Empirical Study ◽  
Software Development ◽  
Adaptive Software ◽  
Development Capability

PERANCANGAN APLIKASI SISTEM PENDUKUNG KEPUTUSAN UNTUK PEMILIHAN KAMERA DENGAN ADAPTIVE SOFTWARE DEVELOPMENT DAN WEIGHTIED

e-NARODROID ◽  
2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Made Kamisutara
Keyword(s):  
Software Development ◽  
Adaptive Software

Perkembangan fotografi di masyarakat sangat berkembang pesat baik di perkotaan maupun di daerah. Seiring dengan perkembangan fotografi tersebut diikuti pula oleh produksi kamera yang bermacam-macam, sesuai dengan kebutuhan. Permasalahan yang mucul adalah, masyarakat sering bingung memilih kamera yang sesuai dan tepat untuk kebutuhannya, untuk itu penelitian ini ingin mengupas dan membuat suatu system pendukung keputusan untuk memilih kamera. Metodologi yang akan digunakan dengan menggunakan adative software development dan metode perengkingan dengan weihtied product. Bahasa pemrograman yang akan digunakan berbasis web, sehingga bisa dipergunakan oleh masyarakat baik di perkotaan maupun daerah-daerah. Kata kunci: sistem pendukung keputusan, adative software development, weighted product, berbasis web

Comparing Software Bugs in Clone and Non-clone Code: An Empirical Study

2017 ◽  
Vol 27 (09n10) ◽  
pp. 1507-1527
Author(s):  
Judith F. Islam ◽  
Manishankar Mondal ◽  
Chanchal K. Roy ◽  
Kevin A. Schneider
Keyword(s):  
High Risk ◽  
Empirical Study ◽  
Software Development ◽  
Comparative Study ◽  
Software Maintenance ◽  
Statistical Significance ◽  
Ongoing Debate ◽  
Software Bugs ◽  
Bug Fixing

Code cloning is a recurrent operation in everyday software development. Whether it is a good or bad practice is an ongoing debate among researchers and developers for the last few decades. In this paper, we conduct a comparative study on bug-proneness in clone code and non-clone code by analyzing commit logs. According to our inspection of thousands of revisions of seven diverse subject systems, the percentage of changed files due to bug-fix commits is significantly higher in clone code compared with non-clone code. We perform a Mann–Whitney–Wilcoxon (MWW) test to show the statistical significance of our findings. In addition, the possibility of occurrence of severe bugs is higher in clone code than in non-clone code. Bug-fixing changes affecting clone code should be considered more carefully. Finally, our manual investigation shows that clone code containing if-condition and if–else blocks has a high risk of having severing bugs. Changes to such types of clone fragments should be done carefully during software maintenance. According to our findings, clone code appears to be more bug-prone than non-clone code.

Proteus: Language and Runtime Support for Self-Adaptive Software Development

IEEE Software ◽  
2019 ◽  
Vol 36 (2) ◽  
pp. 73-82 ◽  
Author(s):  
Saeid Barati ◽  
Ferenc A. Bartha ◽  
Swarnendu Biswas ◽  
Robert Cartwright ◽  
Adam Duracz ◽  
...  
Keyword(s):  
Software Development ◽  
Adaptive Software ◽  
Runtime Support ◽  
Self Adaptive

An Empirical Study on the Effect of Testing on Code Quality Using Topic Models: A Case Study on Software Development Systems

2017 ◽  
Vol 66 (3) ◽  
pp. 806-824 ◽  
Author(s):  
Tse-Hsun Chen ◽  
Stephen W. Thomas ◽  
Hadi Hemmati ◽  
Meiyappan Nagappan ◽  
Ahmed E. Hassan
Keyword(s):  
Empirical Study ◽  
Software Development ◽  
Topic Models ◽  
Code Quality

scholarly journals Empirical Study of Homogeneous and Heterogeneous Ensemble Models for Software Development Effort Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Mahmoud O. Elish ◽  
Tarek Helmy ◽  
Muhammad Imtiaz Hussain
Keyword(s):  
Empirical Study ◽  
Software Development ◽  
Computational Intelligence ◽  
Accurate Estimation ◽  
Development Effort ◽  
Effort Estimation ◽  
Ensemble Models ◽  
Intelligence Models ◽  
Software Development Effort ◽  
Software Development Effort Estimation

Accurate estimation of software development effort is essential for effective management and control of software development projects. Many software effort estimation methods have been proposed in the literature including computational intelligence models. However, none of the existing models proved to be suitable under all circumstances; that is, their performance varies from one dataset to another. The goal of an ensemble model is to manage each of its individual models’ strengths and weaknesses automatically, leading to the best possible decision being taken overall. In this paper, we have developed different homogeneous and heterogeneous ensembles of optimized hybrid computational intelligence models for software development effort estimation. Different linear and nonlinear combiners have been used to combine the base hybrid learners. We have conducted an empirical study to evaluate and compare the performance of these ensembles using five popular datasets. The results confirm that individual models are not reliable as their performance is inconsistent and unstable across different datasets. Although none of the ensemble models was consistently the best, many of them were frequently among the best models for each dataset. The homogeneous ensemble of support vector regression (SVR), with the nonlinear combiner adaptive neurofuzzy inference systems-subtractive clustering (ANFIS-SC), was the best model when considering the average rank of each model across the five datasets.

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