The Importance of Software Maintenance
In the late 1970s, a famous and widely cited survey study by Lientz and Swanson, exposed the very high fraction of life-cycle costs that were being expended on maintenance. They categorized maintenance activities into four classes:
- Adaptive – modifying the system to cope with changes in the software environment (DBMS, OS)
- Perfective – implementing new or changed user requirements which concern functional enhancements to the software
- Corrective – diagnosing and fixing errors, possibly ones found by users
- Preventive – increasing software maintainability or reliability to prevent problems in the future
The survey showed that around 75% of the maintenance effort was on the first two types, and error correction consumed about 21%. Many subsequent studies suggest a similar magnitude of the problem. Studies show that contribution of end user is crucial during the new requirement data gathering and analysis. And this is the main cause of any problem during software evolution and maintenance. So software maintenance is important because it consumes a large part of the overall lifecycle costs and also the inability to change software quickly and reliably means that business opportunities are lost.
Impact of key adjustment factors on maintenance (sorted in order of maximum positive impact)
| Maintenance Factors | Plus Range |
|---|---|
| Maintenance specialists | 35% |
| High staff experience | 34% |
| Table-driven variables and data | 33% |
| Low complexity of base code | 32% |
| Y2K and special search engines | 30% |
| Code restructuring tools | 29% |
| Re-engineering tools | 27% |
| High level programming languages | 25% |
| Reverse engineering tools | 23% |
| Complexity analysis tools | 20% |
| Defect tracking tools | 20% |
| Y2K “mass update” specialists | 20% |
| Automated change control tools | 18% |
| Unpaid overtime | 18% |
| Quality measurements | 16% |
| Formal base code inspections | 15% |
| Regression test libraries | 15% |
| Excellent response time | 12% |
| Annual training of > 10 days | 12% |
| High management experience | 12% |
| HELP desk automation | 12% |
| No error prone modules | 10% |
| On-line defect reporting | 10% |
| Productivity measurements | 8% |
| Excellent ease of use | 7% |
| User satisfaction measurements | 5% |
| High team morale | 5% |
| Sum | 503% |
Not only are error-prone modules troublesome, but many other factors can degrade performance too. For example, very complex “spaghetti code” is quite difficult to maintain safely. A very common situation which often degrades performance is lack of suitable maintenance tools, such as defect tracking software, change management software, and test library software. Below describe some of the factors and the range of impact on software maintenance.
Impact of key adjustment factors on maintenance (sorted in order of maximum negative impact)
| Maintenance Factors | Minus Range |
|---|---|
| Error prone modules | -50% |
| Embedded variables and data | -45% |
| Staff inexperience | -40% |
| High code complexity | -30% |
| No Y2K of special search engines | -28% |
| Manual change control methods | -27% |
| Low level programming languages | -25% |
| No defect tracking tools | -24% |
| No Y2K “mass update” specialists | -22% |
| Poor ease of use | -18% |
| No quality measurements | -18% |
| No maintenance specialists | -18% |
| Poor response time | -16% |
| No code inspections | -15% |
| No regression test libraries | -15% |
| No help desk automation | -15% |
| No on-line defect reporting | -12% |
| Management inexperience | -15% |
| No code restructuring tools | -10% |
| No annual training | -10% |
| No reengineering tools | -10% |
| No reverse-engineering tools | -10% |
| No complexity analysis tools | -10% |
| No productivity measurements | -7% |
| Poor team morale | -6% |
| No user satisfaction measurements | -4% |
| No unpaid overtime | 0% |
| Sum | -500% |
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