Browsing by Author "Adtha Lawanna"
  • Item
    Deletion and Selection Model for Test Case Selection
    ( 2016) Adtha Lawanna ; Jittima Wongwuttiwat ; Benjawan Srisura
    Deletion and selection model is planned for improving the ability of selecting the relevant test cases of the modified programs by comparing the outcomes with the well-known traditional methods, which are random, dataflow, slicing, model based, code based, and coverage based analysis. This is because after adapting the programs for several times, numbers of test case will increase that can drop the whole performance of the new version. The problems are time consuming and producing bugs and other failures when using the updated software. The proposed model consists two main algorithms, which are deletion and selection regarding classifying types of the requirements. According to this, the percent of reducing the size by using the proposed model is better than the traditional methods. Besides, percent of finding bugs of the six comparative studies are higher than the offered technique approximately 55%. Moreover, the efficiency of applying the proposed method is greater than the existing technique as about 86 %.
  • Item
    An Effective Model for Case-Based Maintenance in Cased-Based Reasoning Systems
    ( 2015-11) Adtha Lawanna
    Case-based reasoning systems have been applied for machine learning, artificial intelligence, knowledge-based systems and other related fields in order to provide the right solution to the right problem regarding the four processes, which are the process to retrieve, reuse, revise, and retain cases. This paper focuses on the last process because it produces two main problems, which are the size of a case base increase and the ability of preserving the competency decreases. These critical issues are occurring when repeating the cycles of case-based reasoning. Consequently, the case-based maintenance methods are developed to handle the situations. Accordingly, this paper proposes an effective model for case-based maintenance in casebased reasoning systems to give the best results compared with random, utility, footprint, footprint and utility deletion including case addition algorithm. By running the seven comparative studies on ten datasets retrieved from the machine learning repository, especially to study the efficiency of each algorithm in terms of reducing the size of the case base by selecting the small number of case solutions and preserving the competency after the maintenance systems are applied. According to experimental results, the effectiveness of the proposed model for storing the number of case solution gives the lower size of a case base, when compared with the existing techniques about 34.34%-114.84%. Besides, the percentage of adapting solutions for the traditional methods are lower than the proposed model as about 1.12-6.64 times, including the percent solving problem is lower than the effective model approximately 4.73%-33.55%.
  • Item
    An Effective Test Case Selection for Software Testing Improvement
    ( 2015-11) Adtha Lawanna
    One problem of testing software is selecting the suitable test cases from the test suit regarding the size of the programs. If the size of selected test cases is big, then it can affect the whole performance of software development life cycle. Accordingly, it increases testing time and produce many bugs. Therefore, this paper proposes the improvement of software testing for selecting the appropriate and small number of test cases by considering the amounts of the functions modified, lines of code changed, and numbers of bugs produced after modifying programs. The reason of proposing the software testing improvement model is to prepare effective algorithm, while numbers of bugs are lower than the traditional methods. According to the experimental results, the size of the selected test cases by using the proposed model is less than Retest All, Random, and a Safe Test about 98.70%, 87.86%, and 84.67% respectively. Moreover, the ability of STI is higher than the comparative studies about 1-20 times regarding the number of bugs found after modifying a program.
  • Item
    False Test Case Selection: Improvement of Regression Testing Approach
    ( 2016) Benjawan Srisura ; Adtha Lawanna
    Regression testing has been considered as a time-consumed process in software testing. In a recent year, one of interesting research work initiated for minimizing testing time is finding a technique in selecting test cases from a large test suit. Most of test cases selection technique in literature considers test cases that are related to the requirement's changed. During executing test cases that are related to the modified part, a set of fail test case is accidentally emerged and make test suit has become larger. Therefore, this paper proposes a technique in selecting suitable false test cases when they are generated in regression testing. However, in order to ensure that the quality and validity of using the proposed technique are acceptable, an experiment was systematically conducted in this study. And we also found that the false test case selection technique can minimize the size of test suit, effectively.
  • Item
    Filtering Test Case Selection for Increasing the Performance of Regression Testing
    ( 2016) Adtha Lawanna
    Under the concept of using software testing, the problems are preparing the smallest size of the selected test cases, fixing bugs, predicting the estimated testing time and numbers of the tester. The traditional methods are developed to solve these issues. Unfortunately, they cannot be applied for all reasons. Therefore, the filtering test case selection model is proposed to solve these problems and to increase the efficiency of the regression testing. It prepares the methods of filtering, classifying, and selecting the appropriate test cases. Accordingly, it gives the smaller size of the selected test cases than the traditional selections as 0.23–3.96%. When consider the fault rate measured by the developed technique is also less than those methods equivalent to 13–69%. Furthermore, the testing time and the amount of testers are also less than the comparative studies as about 2–71%, and 13–69% respectively.
  • Item
  • Item
    IT Design Skills Selection for Professional Development
    ( 2016-02)
    IT professionals in these days need to obtain multidisciplinary skill sets in order to succeed in their career. Design skill is considered as one of important characteristic for IT professionals. Many researches addressed design skill sets differently. This results in an increasing numbers of design skill items, in which some of those items have their meaning slightly different from each other. These long lists of design skill items can cause to long range of questionnaire. This study evaluates three techniques used for items selection which help reducing the number of skill items but with expect to remain their value within the selected items. The comparative studies used in this work are random selection, statistics selection, and effective coverage-based selection. The study found that the proposed technique provides the most satisfy result comparing to the other two techniques. According to this, the reduction rate by using this model is better than random and statistics algorithms about 47.83% and 34.78% respectively. Moreover, the percent coverage by using the proposed model is higher than the traditional methods approximately 13-26 %.
  • Item