when attempting to maintain the software. This can make it more difficult to reliably estimate the cost and duration of software maintenance efforts. refers to the ability of managers and maintainers to estimate when Predictability maintenance tasks will need to be performed [12]. Some applications are regularly maintained (e.g., updates to an accounting application are regularly updated with new tax laws), while others are updated at unexpected intervals due to novel changes in the organization’s operating environment. As the ability to anticipate such maintenance is decreased, it becomes more complex to manage the maintenance process as preferred, or even required, resources may be unavailable when critical maintenance tasks need to be performed. refers to the overall size of the maintenance task to be performed as measured Magnitude by lines of code added during the maintenance task [12]. Some modifications are extensive, while others are minor or incremental. Given that larger modification sizes tend to incorporate more code and therefore are more likely to affect interoperability, larger maintenance tasks can be expected, all else being equal, to decrease the ability of maintainers to correctly update the code, as well as the ability of management to successfully oversee such tasks. Software volatility could change the inherent complexity associated with updating a software application, and this may have important implications for the effectiveness of the knowledge-sharing approaches adopted by management to facilitate corrective software maintenance [9, 10, 11, 12, 13]. Thus software volatility may serve as a key moderator of the relationship between technology-, experience- or skill-based approaches to knowledge sharing and corrective software maintenance performance. In this paper, we formally investigate whether this is the case, asking the following overall research question: How do software volatility patterns moderate the effectiveness of technology-, experience- and skill-based approaches to knowledge sharing in corrective software maintenance?
when attempting to maintain the software. This can make it more difficult to reliably estimate the cost and duration of software maintenance efforts. Predictability refers to the ability of managers and maintainers to estimate when maintenance tasks will need to be performed [12]. Some applications are regularly maintained (e.g., updates to an accounting application are regularly updated with new tax laws), while others are updated at unexpected intervals due to novel changes in the organization’s operating environment. As the ability to anticipate such maintenance is decreased, it becomes more complex to manage the maintenance process as preferred, or even required, resources may be unavailable when critical maintenance tasks need to be performed. Magnitude refers to the overall size of the maintenance task to be performed as measured by lines of code added during the maintenance task [12]. Some modifications are extensive, while others are minor or incremental. Given that larger modification sizes tend to incorporate more code and therefore are more likely to affect interoperability, larger maintenance tasks can be expected, all else being equal, to decrease the ability of maintainers to correctly update the code, as well as the ability of management to successfully oversee such tasks. Software volatility could change the inherent complexity associated with updating a software application, and this may have important implications for the effectiveness of the knowledge-sharing approaches adopted by management to facilitate corrective software maintenance [9, 10, 11, 12, 13]. Thus software volatility may serve as a key moderator of the relationship between technology-, experience- or skill-based approaches to knowledge sharing and corrective software maintenance performance. In this paper, we formally investigate whether this is the case, asking the following overall research question: How do software volatility patterns moderate the effectiveness of technology-, experience- and skill-based approaches to knowledge sharing in corrective software maintenance?
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