# Applying Process Mining: The Reality of a Software Development SME
## 1 Introduction  
The successful implementation of Process Mining in large companies has motivated small and medium-sized enterprises (SMEs) to make efforts to extract data from their information systems in order to compare their documented processes with the processes observed by mining techniques [1] and expertise in process management. In the software industry, SMEs represent more than 80% of the workforce [2] and have specific characteristics depending on the capability level of their processes and the integration of their human, material, and technological resources. Software development SMEs face cost and tight delivery deadlines projects [3], the need to use various specific tools (such as management systems) to carry out the software development process [4], and their adaptation to a dynamic business environment [5; 6]. For them, this reality represents a significant challenge in the implementation of Process Mining [5; 7]. Process Mining, as a growing discipline of data science, aims to extract event data recorded in information systems [8; 9; 10; 11]. Most of the data is scattered across various systems,without an explicit relationship between them and the executed process activities [12], resulting in unstructured information. Software process management systems, such as the Issue Tracking System (Jira) and the Version Control System (Github), require additional practices to link their stored data. In the absence of an explicit link between management systems, the available data will need efforts for extraction and structuring through tools or Application Programming Interfaces (APIs). Data extraction can be applied after understanding the data structure and system design [13]. The activities Extraction, Transformation, and Load (ETL) of event logs are the first step to applying Process Mining [8; 11; 13]. The extracted data are transformed into a standard data format and loaded into a system or database [11] as event logs. A software process event log is used in Process Mining to describe the information of events and attributes [14], which can vary depending on the type of process management system. Some attributes of software processes may include the identifier and name of the activity (requirements, architecture, design, implementation, testing, operation, etc. ), the description, the date and time it was recorded, the author (the developer who recorded it), the status (open, close, in progress, etc. ), the priority, and the assignment to a specific team or person. This set of attributes can be used to perform three types of Process Mining: discovery, conformance, and enhancement [11]. Discovery allows obtaining real process models from the event log with the application of algorithms; for example, Inductive Miner, Heuristics Miner, and Fuzzy Miner [11]. Conformance compares the event log and the process model (documented or discovered) through conformance-checking techniques (rule checking, token replay, and alignments) that produce an explicit description of the consistent and deviated parts of the process [15]. By linking an event log and a process model through a conformance technique, the understanding of the process can be improved, including techniques for enhancement [16]. Process enhancement aims for models to be accurate and have high fitness. Generally, the literature on enhancement mining is divided into two types: process extension and process improvement [11; 16]. Process extension focuses on accuracy and aims to incorporate different perspectives such as data (case), resource (organizational), and time perspectives, based on the presence of attributes associated with events [11; 16]. Their inclusion in process models allows for refining the model specifications, thus obtaining extended models with higher levels of accuracy [16]. Regarding process improvement, it seeks process models (i) to better reflect reality, and/or (ii) to only allow executions that are valid from a domain viewpoint, and/or are correlated to better performances [16].