## 1 Introduction  
Traditionally, industry and academia have relegated software maintenance to the background, assigning greater relevance to the development stage [1]. In [2], it was reported that a significant number of institutions that work and depend on software operation and maintenance disregard the implications of this stage in the environment in which the software is useful. This hegemonic nature of the software development stage has directly affected the training of software engineers who are exclusively dedicated to software maintenance, indicating difficulties in finding software engineers specialized in software maintenance [3, 4]. This is reflected in the paucity of evidence of education initiatives focused on software maintenance, which have achieved only relative success, as described in [5].  
From the point of view of the costs associated with the maintenance stage, these are substantially higher than the costs involved in the development stage [6]. The software industry has been aware of this concern for a long time [7]. Previous studies have suggested that the costs of the maintenance stage are close to 40% to 80% [8] and can even bankrupt a software project if the management of the technical debt is not adequate by generating interest that consumes the budget to be assigned to new features [9].  
In recent years, the ML boom has promoted the advance of software production. Nevertheless, the attempts to incorporate it to improve maintainability, which is considered a critical quality attribute for far-reaching projects, have yielded inconclusive results, as indicated in [10]. One of the issues detected, for example, points to each study proposing a new model from zero, without expanding on or working from the existing models. Theyhave no independent validation; they are only validated by their authors and are impossible to replicate because their datasets are private or inaccessible. Additionally, the increase in the complexities of software has taken a step toward increasing the data to be analyzed, but the metrics associated with maintainability have not kept pace [11].  
In the extent to which it has been explored in the last 10 years, there has been no effort to review the trends of the intersection between ML techniques and software maintenance. Therefore, our study will make it easier for researchers and industry professionals to make evidence-based decisions by considering potential gaps or areas with abundant studies that can be consolidated to generate certainties based on research.  
Given this context, the objectives of our study are: (1) to identify the extent to which the trends in ML are present in studies related to software maintenance and to (2) characterize the intersection between ML and software maintenance, as well as its context. This article presents a systematic mapping study (SMS) that was developed under the protocol proposed by [PERSON] [12; 13]. The review and analysis of hundreds of articles enabled us to establish the main study topics within the discipline, which are the areas that have not been addressed and what we should work on in the future.  
Our study is organized as follows: Section 2 presents the incorporation of ML into software maintenance; Section 3 presents the systematic mapping method used; Section 4 describes the main results of the study; Section 5 provides the answers to the proposed research questions, and, finally, the conclusions of our study are offered in Section 7.