As a developer, it is often complex to foresee the impact of changes in source code on usage, e.g., it is time-consuming to find out all components that will be impacted by a change or estimate the impact on the usability of a failing piece of code. It is therefore hard to decide how much effort in quality assurance is justifiable to obtain the desired business goals. In this paper, to reduce the difficulty for developers to understand the importance of source code, we propose an automated way to provide this information to developers as they are working on a given piece of code. As a proof-of-concept, we developed a plug-in for Microsoft Visual Studio Code that informs about the importance of source code methods based on the frequency of usage by the end-users of the developed software. The plug-in aims to increase the awareness developers have about the importance of source code in an unobtrusive way, helping them to prioritize their effort to quality assurance, technical excellence, and usability. code_call_lens can be downloaded from GitHub at https://github.com/xxMUROxx/vscode.code_call_lens.

Background. Process mining is a technique to build process models from "execution logs" (i.e., events triggered by the execution of a process). State-of-the-art tools can provide process managers with different graphical representations of such models. Managers use these models to compare them with an ideal process model or to support process improvement. They typically select the representation based on their experience and knowledge of the system. Aim. This work studies how to automatically build process models representing the actual intents (or uses) of users while interacting with a software system. Such intents are expressed as a set of actions performed by a user to a system to achieve specific use goals. Method. This work applies the theory of Hidden Markov Models to mine use logs and automatically model the use of a system. Results. Unlike the models generated with process mining tools, the Hidden Markov Models automatically generated in this study provide the intents of a user and can be used to recommend managers with a faithful representation of the use of their systems. Conclusions. The automatic generation of the Hidden Markov Models can achieve a good level of accuracy in representing the actual user's intents provided the log dataset is carefully chosen. In our study, the information contained in one-month set of logs helped automatically build Hidden Markov Models with superior accuracy and similar expressiveness of the models built together with the company's stakeholder.

Characterizing how users interact with software has many applications. For example, to understand which features are used, in which sequence operations are performed, etc. can help to understand how the user interface could be improved, to identify missing features, or to identify scenarios which are good candidates for test cases. This paper presents an industrial case study in which we investigate how users interact with an enterprise resource planning software using process mining. Our case study illustrates how we identify user interaction processes, the encountered advantages, and the faced challenges. One of the major findings is that the decision how to group events into cases is crucial for the application of the method.