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All Journal Perfecting a Video Game with Game Metrics IJEBD (International Journal Of Entrepreneurship And Business Development) Indonesian Journal of Electrical Engineering and Computer Science
Zailani Abdullah
Universiti Malaysia Kelantan
Computer Science & IT Economics, Econometrics & Finance

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Journal : Perfecting a Video Game with Game Metrics

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Regression test selection model: a comparison between ReTSE and pythia Amir Ngah; Malcolm Munro; Zailani Abdullah; Masita A. Jalil; Mohamad Abdallah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.10332

Abstract

As software systems change and evolve over time regression tests have to be run to validate these changes. Regression testing is an expensive but essential activity in software maintenance. The purpose of this paper is to compare a new regression test selection model called ReTSE with Pythia. The ReTSE model uses decomposition slicing in order to identify the relevant regression tests. Decomposition slicing provides a technique that is capable of identifying the unchanged parts of a system. Pythia is a regression test selection technique based on textual differencing. Both techniques are compare using a Power program taken from Vokolos and Frankl’s paper. The analysis of this comparison has shown promising results in reducing the number of tests to be run after changes are introduced.
i-Eclat: performance enhancement of eclat via incremental approach in frequent itemset mining Wan Aezwani Wan Abu Bakar; Mustafa Man; Mahadi Man; Zailani Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.13497

Abstract

One example of the state-of-the-art vertical rule mining technique is called equivalence class transformation (Eclat) algorithm. Neither horizontal nor vertical data format, both are still suffering from the huge memory consumption. In response to the promising results of mining in a higher volume of data from a vertical format, and taking consideration of dynamic transaction of data in a database, the research proposes a performance enhancement of Eclat algorithm that relies on incremental approach called an Incremental-Eclat (i-Eclat) algorithm. Motivated from the fast intersection in Eclat, this algorithm of performance enhancement adopts via my structured query language (MySQL) database management system (DBMS) as its platform. It serves as the association rule mining database engine in testing benchmark frequent itemset mining (FIMI) datasets from online repository. The MySQL DBMS is chosen in order to reduce the preprocessing stages of datasets. The experimental results indicate that the proposed algorithm outperforms the traditional Eclat with 17% both in chess and T10I4D100K, 69% in mushroom, 5% and 8% in pumsb_star and retail datasets. Thus, among five (5) dense and sparse datasets, the average performance of i-Eclat is concluded to be 23% better than Eclat.
Co-Authors Amir Ngah Mahadi Man Malcolm Munro Masita A. Jalil Masita@Masila Abd Jalil Mohamad Abdallah Mustafa Bin Man Mustafa Man Noorshella Che Nawi Tutut Herawan Wan Aezwani Wan Abu Bakar Wan Aezwani Wan Abu Bakar