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Software maintenance in software engineering is the modification of a software product after delivery to correct faults, to improve performance or other attributes. A common perception of maintenance is that it merely involves fixing defects. However, one study indicated that over 80% of maintenance effort is used for non-corrective actions. This perception is perpetuated by users submitting problem reports that in reality are functionality enhancements to the system. More recent studies put the bug-fixing proportion closer to 21%. Software maintenance and evolution of systems was first addressed by Meir M. Lehman in 1969. Over a period of twenty years, his research led to the formulation of Lehman's Laws (Lehman 1997). Key findings of his research conclude that maintenance is really evolutionary development and that maintenance decisions are aided by understanding what happens to systems (and software) over time. Lehman demonstrated that systems continue to evolve over time. As they evolve, they grow more complex unless some action such as code refactoring is taken to reduce the complexity. 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. 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 problem magnitude. Studies show that contribution of end users is crucial during the new requirement data gathering and analysis. This is the main cause of any problem during software evolution and maintenance. 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. The key software maintenance issues are managerial and technical. Management issues include alignment with customer priorities, staffing, assigning responsibilities, and estimating costs.

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Code refactoring

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Data-based model maintenance in the era of industry 4.0: A methodology

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Despite the high number of investments for data-based models in the expansion of Industry 4.0, too little effort has been made to ensure the maintenance of those models. In a data-streaming environment, data-based models are subject to concept drifts. A concept drift is a change in data distribution which will, at some point, decrease the accuracy of the model. To address this problem, various frameworks are presented in the literature, but there is no optimal methodology for implementing them. This paper presents a methodology to implement a problem-oriented complete solution to ensure the maintenance of an industrial data-based model. The final drift-handling solution is composed of a sampling decision system and an update system. The methodology begins with a concept-drift identification phase. Solutions are then pre-selected based on the identified concept drifts. Next, an optimization problem is designed to select the solution that optimizes the costs and respects the constraints. To better link the concept drift characteristics and the drift-handling solutions, a causal concept-drift classification system is proposed. The industrial implementation of such a solution is discussed and several questions are raised. This paper presents an original and detailed methodology that shows encouraging results to address the model-maintenance challenge; however, concept drift identification, and links between concept-drift charac-teristics and drift detection, require further research.

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