## 1 Introduction  
The study of GT in software development environments is challenging due to the elusive classification of GT. The variety of versions, diverse forms of application, and the relative novelty of qualitative analysis in software engineering, combined with the multitude of knowledge fields in which GT can be used, make its use difficult. In software engineering, reviewers and expert editors still face difficulties in understanding the nature and boundaries of GT. Researchers tend to be overly cautious in writing their articles and provide excessive explanations of and justifications for the GT method [1]. This conservative approach may be due to the perception that engineers receive insufficient training in the research methodologies of social sciences [2]. As a result, there are limited literature reviews on GT in software development and related topics, though the number of scientific publications that focus on literature reviews and systematic mapping is increasing [3, 4, 5]. It is clear from various bibliographic databases that the main challenge is the difficulty in classifying scientific papers in software development within the variants of GT [3, 4, 5], which has hindered the ability to conduct a deeper analysis. This lack of clear classification has led to difficulties in comparing and synthesizing findings across studies, limiting the depth of analysis and hindering the advancement of knowledge in the field of software development. Qualitative analysis, including GT, has paved the way for new methods of studying and analyzing software development. GT provides new insights into the study of humans and their environment [6; 7; 8], particularly regarding the cost of a project and the performance of software equipment [2]. Data are a crucial aspect in both software development and GT. However, the relationship between data in GT and software development has not been thoroughly examined. Software development deals with a wide variety of data that impacts the various aspects of its processes. At the same time, GT involves the collection and analysis of data until theories emerge and saturation is reached [2; 5; 9]. GT is typically utilized in the social study of individuals, groups, or communities. However, when applied to software engineering, GT faces new challenges as it becomes a socio-technical issue [10]; in this context, the subjective (sociological) aspect is intertwined with the objective (technical) aspect [10]. This convergence of social and technical aspects adds complexity to the GT analysis in the context of software engineering. In software development, various types of data are processed, including both the volume of information and data related to processes, activities, artifacts, and the individuals and teams involved. Additionally, a diverse range of data [4] is used in the GT studies applied to software development (all data are relevant) [6; 8; 11]. However, this diversity has not been adequately addressed in previous studies. Many articles applying GT in software development do not clearly state which variant of GT they are using or if they are combining variants [5]. There are also some challenges when using GT in software development. Firstly, GT is demanding and requires strict adherence to its methodology, making it difficult to implement in practice [12]. Secondly, the complexity of the processes in software development adds to the difficulty of conducting GT [13]. Additionally, there are several variants of GT, the most prominent being Glaserian and Straussian, which have significant differences [8]. Lastly, it is unclear whether it is appropriate to mix these variants [7] or not [8] when conducting GT research. This research employs the Glaserian systematic mapping study (GSMS) methodology developed by [PERSON] [14] to thoroughly search through selected scientific articles.