Software Quality Assessment Model: A New Approach for Software Testing Tools

被引:0
作者
Zulkifli, Zulkifli [1 ]
Mardiana, Mardiana [2 ]
Despa, Dikpride [3 ]
机构
[1] Aisyah Univ, Fac Technol & Informat, Dept Informat Engn, Lampung, Indonesia
[2] Univ Lampung, Fak Teknik, Teknik Informatika, Bandar Lampung, Indonesia
[3] Univ Lampung, Fak Teknik, Teknik Elekt, Bandar Lampung, Indonesia
关键词
Quality of software; framework; software quality assessment; RELIABILITY;
D O I
10.1142/S0218194024500517
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The process of software testing is a crucial phase in determining the quality of software, and this phase requires significant costs and a considerable amount of time for testers. This paper discusses the development of a framework for software quality assessment, involving flexible choices of software testing methods and variables in the form of an application. The method used is experimental, developing a new framework based on previous research, where previous research was limited to specific methods and testing variables. The result of this research is the creation of a new framework for software quality assessment. It is hoped that this framework can serve as a reference for software companies in evaluating software quality. In terms of complexity, this framework has the advantage of allowing a tester to choose methods with more flexible or unlimited testing variables. Regarding the estimated time and costs, with PF=4,5 and 10, the practical application complexity of the developed framework is estimated to have the best costs, time and human resources at IDR 254,240,000, with an estimated time of 3,178 work hours and 6,356 work hours with a team of 3 people.
引用
收藏
页码:139 / 155
页数:17
相关论文
共 22 条
[1]   Neural network laundering: Removing black-box backdoor watermarks from deep neural networks [J].
Aiken, William ;
Kim, Hyoungshick ;
Woo, Simon ;
Ryoo, Jungwoo .
COMPUTERS & SECURITY, 2021, 106
[2]  
Akta A. Z., 2021, GAZI U J SCI PART EN, V8, P1
[3]   Towards a New Framework of Software Reliability Measurement Based on Software Metrics [J].
Amara, Dalila ;
Rabai, Latifa Ben Arfa .
8TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT-2017) AND THE 7TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT 2017), 2017, 109 :725-730
[4]   Machine Learning Applied to Software Testing: A Systematic Mapping Study [J].
Durelli, Vinicius H. S. ;
Durelli, Rafael S. ;
Borges, Simone S. ;
Endo, Andre T. ;
Eler, Marcelo M. ;
Dias, Diego R. C. ;
Guimaraes, Marcelo P. .
IEEE TRANSACTIONS ON RELIABILITY, 2019, 68 (03) :1189-1212
[5]  
Fitriana F, 2022, INT J ADV COMPUT SC, V13, P131
[6]  
Galimova E. Y., 2021, IOP C SER MAT SCI EN, V1019, P1
[7]   A Tailored Domain Analysis Method for the Development of System-Specific Testing DSLs Enabling their Smooth Introduction in Automotive Practice [J].
Juhnke, Katharina ;
Tichy, Matthias .
2019 45TH EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA 2019), 2019, :10-18
[8]  
Khan R., 2019, INT J INNOV TECHNOL, V8, P2055
[9]   The impact of Software Testing education on code reliability: An empirical assessment [J].
Lazzarini Lemos, Otavio Augusto ;
Silveira, Fabio Fagundes ;
Ferrari, Fabiano Cutigi ;
Garcia, Alessandro .
JOURNAL OF SYSTEMS AND SOFTWARE, 2018, 137 :497-511
[10]  
Mehta S., 2021, US Patent Application, Patent No. [17/011,960, 17011960]