It is common practice for users to provide feedback on apps through social media or app store reviews.This feedback is a rich source of requirements for these apps.However, manually analyzing vast amounts of user feedback is unfeasible, so automated user feedback classifiers are useful tools.This research work presents a user feedback classifier based on Machine Learning (ML) for the classification of reviews according to software quality characteristics complaint with the ISO25010 standard.We developed this approach by testing several ML algorithms, features, and class balancing techniques for classifying user feedback on a data set of 1500 reviews.The maximum F1 and F2 scores obtained were 60% and 73%, with recall as high as 94%.This approach does not replace human specialists, but reduces the effort required for requirements elicitation.

Business process descriptions are useful documents that are becoming increasingly important for identifying and documenting business processes. They are particularly beneficial during discovery when information about the process is gathered in interviews or by observation. Such business process descriptions are written as natural language text, which makes them intrinsically ambiguous. For this reason, it is the major challenge to formulate them in a precise and correct way right from the start. Therefore, this paper presents a service oriented architecture that analyzes a process description written in natural language to generate a sound process description. Being sound means that a description is structured, unambiguous, reveals possible quality and soundness problems related to BPMN 2.0, and contains clear identifiers for all known process elements in the original text. More specifically, we develop specific analysis and transformation techniques that are integrated by our proposed architecture. For validation purposes, we have implemented a prototype of this architecture. Our evaluation demonstrates that our techniques to generate sound process descriptions cover an average of 95% of the information extracted from its original process description while maintaining quality properties. Finally, our architecture can be enhanced with additional services that contribute to the creation and management of processes descriptions in organizations.

Techniques of functional size measurement are easily found in the literature, however, in the evaluation process of these techniques is not always approached which makes its validity questionable. The evaluation of the Business Process Point Analysis (BPPA) technique is the object of study of this article that aims to consistently evaluate its reproducibility and accuracy, identifying its limitations. BPPA was proposed so that project managers can systematically estimate the functional size of a business process automation project. Thus, this article presents the execution of a quasi-experiment realized with 58 graduate and postgraduate students, who measured the functional size of three business process models. The results of this experiment present the low reproducibility and accuracy of the technique as well as its limitations.

Modern techniques widely applied in data mining, including computational intelligence and machine learning, have been fairly neglected in process mining. We conducted an exploratory study to use artificial neural networks to extract knowledge from an unstructured process in the distance learning domain. We discuss some possible benefits and limitations regarding the mining of unstructured processes. Results suggest that applying either classical process mining or modern data mining techniques would result in significant benefits for this domain. Our work helps to guide new studies related to the application of modern mining techniques in process mining.

Several approaches have already been proposed to extract both business processes and business rules from a legacy source code. These approaches consider static source code analysis for the extraction procedure. However, business processes have components that can not be directly extracted by static analysis (i.e., participants, responsibilities, and concurrent activities). Moreover, well-known static analysis algorithms do not support the incremental extraction of information from the legacy code. Large legacy systems can benefit from an incremental analysis strategy in order to provide iterative information extraction as well as to achieve partial results much earlier. This paper discusses a new approach for business knowledge extraction from legacy systems. The approach considers an incremental process mining technique to extract business process structures and the business rules associated to it. Discovery results can be used in various ways by business analysts and software architects, e.g. documentation of legacy systems or for re-engineering purposes.