Bug Report Summarization by Using Swarm Intelligence Approaches

2020 ◽  
Vol 13 (1) ◽  
pp. 53-67
Author(s):  
Ashima Kukkar ◽  
Rajni Mohana
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Particle Swarm ◽  
Feature Weighting ◽  
Training Dataset ◽  
Hybrid Swarm ◽  
Data Set ◽  
Swarm Optimization ◽  
Bug Reports ◽  
Bug Report

Background: Bug reports are considered as a reference document, during the maintenance phase of the software development process. The developer's counsel them at whatever point they have to know about the reported bug and need to explore past bug solution. This process requires a sustainable amount of time due to a large number of comments. Therefore, the best solution to prevent the developers from reading the whole bug report is to summarize the entire discussion in a couple of sentences. Bug report summarization is the extraction of some important part of bug reports that are useful for investigation, resolving the bug with similar problems, reproducing the bug and checking the status of the bug. If the bug reports have huge volume, variety and velocity information as big data, extractive bug report summarization would be emerging as an issue. However, the examiners, find out the bug report summaries do not meet the expectation of the developer; there is a still need for reading the entire discussion. Objective: (1) To generate generalized unsupervised extractive bug report summarization system, which is easily applicable on any dataset without the need of effort and cost of manually creating summaries for training dataset (2) To handle the extensive number of comments and generate short summaries. (3) To reduce the data sparsity, reduction of information, redundancy and convergence issue for short and lengthy data set. (4) To achieve the semantic summarization solution and explore the large search space. (5) To provide the facility of adjusting the summary percentage. Methods: Particle swarm optimization and Hybridization of Ant Colony Optimization and Particle Swarm Optimization approaches are used with the advantage of feature weighting technique. Conclusion: The efficiency of the proposed approaches are compared with the best existing supervised and unsupervised approaches. The result shows that the Hybrid swarm intelligence approach provided better.

scholarly journals Correlation with the fundamental PSO and PSO modifications to be hybrid swarm optimization

2021 ◽  
pp. 25-32
Author(s):  
Raed A Hasan ◽  
Suhel Shahab Najim ◽  
Munef Abdullah Ahmed
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Modeling ◽  
Swarm Intelligence ◽  
Low Cost ◽  
Particle Swarm ◽  
Single Species ◽  
Hybrid Swarm ◽  
Fundamental Particle ◽  
Swarm Optimization ◽  
Suboptimal Solutions

A swarm is a group of a single species in which the members interact with one another and with the immediate environment without a principle for control or the emergence of a global intriguing behavior. Swarm-based metaheuristics, including nature-inspired populace-based methods, have been developed to aid the creation of quick, robust, and low-cost solutions for complex problems. Swarm intelligence was proposed as a computational modeling of swarms and has been successfully applied to numerous optimization tasks since its introduction. A correlation with the fundamental Particle Swarm Optimization (PSO) and PSO modifications demonstrates that hybrid swarm optimization outperforms existing strategies. The downside of hybrid swarm optimization is that it frequently tends to arrive at suboptimal solutions. As such, efforts are being made into combining HSO and other algorithms to arrive at better quality solutions

From Optimization to Clustering

2015 ◽  
pp. 594-619 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

From Optimization to Clustering

2016 ◽  
pp. 1519-1544 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

scholarly journals PSOVina: The hybrid particle swarm optimization algorithm for protein–ligand docking

2015 ◽  
Vol 13 (03) ◽  
pp. 1541007 ◽  
Author(s):  
Marcus C. K. Ng ◽  
Simon Fong ◽  
Shirley W. I. Siu
Keyword(s):  
Particle Swarm Optimization ◽  
Virtual Screening ◽  
Particle Swarm ◽  
Ligand Docking ◽  
Conformational Search ◽  
Search Problem ◽  
Data Set ◽  
Swarm Optimization ◽  
Screening Experiments ◽  
Hybrid Particle Swarm Optimization

Protein–ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden–Fletcher–Goldfarb–Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein–ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51–60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein–ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo .

Designing a Multi Echelon Flexible Logistics Network Using Co-Evolutionary Immune-Particle Swarm Optimization with Penetrated Hyper-Mutation (COIPSO-PHM)

2011 ◽  
Vol 110-116 ◽  
pp. 3713-3719
Author(s):  
N. C. Hiremath ◽  
Sadanand Sahu ◽  
Manoj Kumar Tiwari
Keyword(s):  
Particle Swarm Optimization ◽  
Real Life ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Data Set ◽  
Swarm Optimization ◽  
Automotive Manufacturing ◽  
Logistics Network ◽  
Delivery Modes ◽  
Flexible Delivery

The strategic design and operation of outbound logistics network in an automotive manufacturing supply chain is directly related with the competitive strategy adopted by the firm. We discuss here an outbound logistics network model with four echelons and flexible delivery modes by incorporating cross-dock facility in the network. The paper aims to achieve a minimum total logistics cost for flexible delivery modes adopted in the network. The mathematical model is formulated as a mixed integer programming model and solved by using a hybrid algorithm named co-evolutionary immune-particle swarm optimization with penetrated hyper-mutation (COIPSO-PHM). The proposed model is combinatorial in nature owing to varying problem instances. The proposed solution methodology is tested on a sample data set mimicking the real life situation and the results are found to be satisfactory.

scholarly journals A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Lizhi Cui ◽  
Zhihao Ling ◽  
Josiah Poon ◽  
Simon K. Poon ◽  
Junbin Gao ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
High Performance ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Physical Principle ◽  
Reference Curve ◽  
Optimal Parameters ◽  
General Reference ◽  
Data Set ◽  
Swarm Optimization

This paper proposes a separation method, based on the model of Generalized Reference Curve Measurement and the algorithm of Particle Swarm Optimization (GRCM-PSO), for the High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD) data set. Firstly, initial parameters are generated to construct reference curves for the chromatogram peaks of the compounds based on its physical principle. Then, a General Reference Curve Measurement (GRCM) model is designed to transform these parameters to scalar values, which indicate the fitness for all parameters. Thirdly, rough solutions are found by searching individual target for every parameter, and reinitialization only around these rough solutions is executed. Then, the Particle Swarm Optimization (PSO) algorithm is adopted to obtain the optimal parameters by minimizing the fitness of these new parameters given by the GRCM model. Finally, spectra for the compounds are estimated based on the optimal parameters and the HPLC-DAD data set. Through simulations and experiments, following conclusions are drawn: (1) the GRCM-PSO method can separate the chromatogram peaks and spectra from the HPLC-DAD data set without knowing the number of the compounds in advance even when severe overlap and white noise exist; (2) the GRCM-PSO method is able to handle the real HPLC-DAD data set.

Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis

2012 ◽  
Vol 57 (5) ◽  
Author(s):  
Mohammad Reza Daliri
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Particle Swarm ◽  
Support Vector ◽  
Emission Computed Tomography ◽  
Binary Particle Swarm Optimization ◽  
Data Set ◽  
Swarm Optimization ◽  
Vector Machines

AbstractIn this article, we propose a feature selection strategy using a binary particle swarm optimization algorithm for the diagnosis of different medical diseases. The support vector machines were used for the fitness function of the binary particle swarm optimization. We evaluated our proposed method on four databases from the machine learning repository, including the single proton emission computed tomography heart database, the Wisconsin breast cancer data set, the Pima Indians diabetes database, and the Dermatology data set. The results indicate that, with selected less number of features, we obtained a higher accuracy in diagnosing heart, cancer, diabetes, and erythematosquamous diseases. The results were compared with the traditional feature selection methods, namely, the F-score and the information gain, and a superior accuracy was obtained with our method. Compared to the genetic algorithm for feature selection, the results of the proposed method show a higher accuracy in all of the data, except in one. In addition, in comparison with other methods that used the same data, our approach has a higher performance using less number of features.

scholarly journals Refined Heuristic Swarm Intelligence Algorithm

2020 ◽  
pp. 267-275
Author(s):  
I. I. Aina ◽  
C. N. Ejieji
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

In this paper, a new metaheuristic algorithm named refined heuristic intelligence swarm (RHIS) algorithm is developed from an existing particle swarm optimization (PSO) algorithm by introducing a disturbing term to the velocity of PSO and modifying the inertia weight, in which the comparison between the two algorithms is also addressed.

scholarly journals OPTIMASI NAÏVE BAYES DENGAN PARTICLE SWARM OPTIMIZATION DAN STRATIFIED UNTUK PREDIKSI KREDIT MACET PADA KOPERASI

2018 ◽  
Vol 1 (1) ◽  
pp. 43-50
Author(s):  
Tati Mardiana
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
T Test ◽  
Data Set ◽  
Swarm Optimization

Dalam bisnis, koperasi memiliki peranan penting dalam meningkatkan perekonomian nasional. Ketidakmampuan anggota untuk membayar angsuran kredit merupakan masalah utama yang terjadi pada koperasi. Akibatnya, terjadi kredit macet. Koperasi dapat menghindari kredit macet dengan membuat prediksi dari anggota koperasi yang berpotensi terlambat membayar kredit. Dalam beberapa penelitian telah menggunakan Naive Bayes untuk masalah klasifikasi karena perhitungan yang efisien, dan  akurasi tinggi. Tetapi Naive Bayes mengasumsikan bahwa semua atribut kelas tidak tergantung pada atribut lainnya. Naive Bayes sesuai untuk masalah klasifikasi dengan atribut besar. Namun, asumsi ini sering tidak dapat dipertahankan dalam masalah klasifikasi nyata. Dalam beberapa dokumen, kinerja Naive Bayes tidak sempurna. Tujuan dari penelitian ini adalah untuk mengoptimalkan metode Naive Bayes menggunakan Particle Swarm Optimization (PSO) dan untuk meningkatkan akurasi dalam memprediksi kredit macet di koperasi. Penelitian ini menggunakan data dari Pusat Data Koperasi (PUSKOPDIT) DKI Jakarta. Data set kredit yang diperoleh sebanyak 565 record dengan 15 prediktor atribut dan 1 atribut kelas. Hasil pengujian dengan confusion matrix dan kurva ROC diperoleh dari nilai akurasi sebesar 86% dan nilai sebesar 0,867 dengan diagnosis klasifikasi baik. Penelitian ini menunjukkan bahwa penggunaan PSO pada NBC untuk memprediksi kredit macet meningkatkan akurasi 21,03% dan AUC sebesar 0,069. Hasil uji T-Test dan Anova menunjukkan bahwa pada dua metode klasifikasi yang diuji memiliki perbedaan yang nyata (signifikan) dalam nilai AUC.

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