Characterizing Software Stability via Change Propagation Simulation
Software stability means the resistance to the amplification of changes in software. It has become one of the most important attributes that affect maintenance cost. To control the maintenance cost, many approaches have been proposed to measure software stability. However, it is still a very difficu...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/9414162 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832550257931583488 |
|---|---|
| author | Weifeng Pan Haibo Jiang Hua Ming Chunlai Chai Bi Chen Hao Li |
| author_facet | Weifeng Pan Haibo Jiang Hua Ming Chunlai Chai Bi Chen Hao Li |
| author_sort | Weifeng Pan |
| collection | DOAJ |
| description | Software stability means the resistance to the amplification of changes in software. It has become one of the most important attributes that affect maintenance cost. To control the maintenance cost, many approaches have been proposed to measure software stability. However, it is still a very difficult task to evaluate the software stability especially when software becomes very large and complex. In this paper, we propose to characterize software stability via change propagation simulation. First, we propose a class coupling network (CCN) to model software structure at the class level. Then, we analyze the change propagation process in the CCN by using a simulation way, and by doing so, we develop a novel metric, SS (software stability), to measure software stability. Our SS metric is validated theoretically using the widely accepted Weyuker’s properties and empirically using a set of open source Java software systems. The theoretical results show that our SS metric satisfies most of Weyuker’s properties with only two exceptions, and the empirical results show that our metric is an effective indicator for software quality improvement and class importance. Empirical results also show that our approach has the ability to be applied to large software systems. |
| format | Article |
| id | doaj-art-1a149bc087f742029af21cb1d523e8af |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-1a149bc087f742029af21cb1d523e8af2025-02-03T06:07:13ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/94141629414162Characterizing Software Stability via Change Propagation SimulationWeifeng Pan0Haibo Jiang1Hua Ming2Chunlai Chai3Bi Chen4Hao Li5School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, ChinaSchool of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, ChinaSchool of Engineering and Computer Science, Oakland University, Rochester, MI 48309, USASchool of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, ChinaSchool of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, ChinaDepartment of Computer Science, Western Michigan University, Kalamazoo, MI 49008, USASoftware stability means the resistance to the amplification of changes in software. It has become one of the most important attributes that affect maintenance cost. To control the maintenance cost, many approaches have been proposed to measure software stability. However, it is still a very difficult task to evaluate the software stability especially when software becomes very large and complex. In this paper, we propose to characterize software stability via change propagation simulation. First, we propose a class coupling network (CCN) to model software structure at the class level. Then, we analyze the change propagation process in the CCN by using a simulation way, and by doing so, we develop a novel metric, SS (software stability), to measure software stability. Our SS metric is validated theoretically using the widely accepted Weyuker’s properties and empirically using a set of open source Java software systems. The theoretical results show that our SS metric satisfies most of Weyuker’s properties with only two exceptions, and the empirical results show that our metric is an effective indicator for software quality improvement and class importance. Empirical results also show that our approach has the ability to be applied to large software systems.http://dx.doi.org/10.1155/2019/9414162 |
| spellingShingle | Weifeng Pan Haibo Jiang Hua Ming Chunlai Chai Bi Chen Hao Li Characterizing Software Stability via Change Propagation Simulation Complexity |
| title | Characterizing Software Stability via Change Propagation Simulation |
| title_full | Characterizing Software Stability via Change Propagation Simulation |
| title_fullStr | Characterizing Software Stability via Change Propagation Simulation |
| title_full_unstemmed | Characterizing Software Stability via Change Propagation Simulation |
| title_short | Characterizing Software Stability via Change Propagation Simulation |
| title_sort | characterizing software stability via change propagation simulation |
| url | http://dx.doi.org/10.1155/2019/9414162 |
| work_keys_str_mv | AT weifengpan characterizingsoftwarestabilityviachangepropagationsimulation AT haibojiang characterizingsoftwarestabilityviachangepropagationsimulation AT huaming characterizingsoftwarestabilityviachangepropagationsimulation AT chunlaichai characterizingsoftwarestabilityviachangepropagationsimulation AT bichen characterizingsoftwarestabilityviachangepropagationsimulation AT haoli characterizingsoftwarestabilityviachangepropagationsimulation |