Amir Ngah

Regression test selection by exclusion using decomposition slicing

This research uses an exclusive technique to reduce the regression tests size. Exclusive technique means that a large number of tests will be excluded and leave a relatively small but safe test set. Decomposition slicing is used to empirically investigate whether it can reduce regression tests by an exclusive technique. Decomposition slicing provides a technique to identify the unchanged parts of the system. An exclusive technique will be analysed using an existing analysing framework to compare with its counterpart, the inclusive technique. This research expects to propose a new safe regression test selection by an exclusion technique using decomposition slicing.

ELP-M2: An efficient model for mining least patterns from data repository

Most of the algorithm and data structure facing a computational problem when they are required to deal with a highly sparse and dense dataset. Therefore, in this paper we proposed a complete model for mining least patterns known as Efficient Least Pattern Mining Model (ELP-M2) with LP-Tree data structure and LP-Growth algorithm. The comparative study is made with the well-know LP-Tree data structure and LP-Growth algorithm. Two benchmarked datasets from FIMI repository called Kosarak and T40I10D100K were employed. The experimental results with the first and second datasets show that the LP-Growth algorithm is more efficient and outperformed the FP-Growth algorithm at 14% and 57%, respectively.

IncSPADE: An Incremental Sequential Pattern Mining Algorithm Based on SPADE Property

In this paper we propose Incremental Sequential PAttern Discovery using Equivalence classes (IncSPADE) algorithm to mine the dynamic database without the requirement of re-scanning the database again. In order to evaluate this algorithm, we conducted the experiments against three different artificial datasets. The result shows that IncSPADE outperformed the benchmarked algorithm called SPADE up to 20%.