Digitalisation nowadays impacts business environments significantly. Therefore, if higher education wants to educate students properly for a successful career path, the business and economics curriculum must be enriched with innovative teaching approaches covering actual topics, such as, for example, the implementation of the ERPsim business simulation, a representative of serious games. The ERPsim business simulation is based on SAP ERP, and is aimed at teaching ERP concepts. It covers different business processes, and allows students to learn using a hands-on approach, simulating real-life scenarios. The presented research focuses on using an ERPsim business simulation as the introduction to the course. The simulation was implemented to ease the introduction of ERP and IT concepts to the business and economic oriented students. Considering the positive effects of serious games and gamification, our research was formed and focused on knowledge acquisition and future student engagement. We researched whether participation in the introductory simulation results in significant knowledge gain and impacts students’ future course engagement. The data were gathered using a self-assessment questionnaire before and after an introductory simulation. The results were highly positive, confirming many positive aspects of business simulation, among others, significant knowledge increase in the domains of business process and ERP transactions, and in the domain of technical knowledge for SAP ERP. The results confirmed that the introductory simulation is appropriate for introducing ERP concepts to newcomers, and, nevertheless, confirmed the positive impact on their intent on future course engagement.

The introduction of enterprise resource planning (ERP) concepts to IT students entails many challenges. Due to the system’s complexity, newcomers need an extensive amount of time to be able to use it independently. Additionally, the learning preferences and characteristics of digital natives differ significantly from previous generations. Therefore, the use of alternative learning approaches is desirable. To achieve the best possible learning outcomes, it is advisable to implement learning approaches that require students’ active participation, for example, an experiential learning approach. A variation is an ERPsim business simulation game, which we used within the ERP systems course. The game is implemented in sequential rounds, whereby each round ends with a review of the collected experiences. The simulation game was used at the beginning of the course to ease the introduction of ERP concepts for IT-related students. This paper is the result of three years of research into the perceived usability of SAP ERP introduced with the business simulation game, combined with the results of a study evaluating students’ opinions, knowledge, and skills. Perceived usability was measured using a System Usability Scale (SUS), while the students’ experiences were gathered using a self-evaluation questionnaire. The study revealed the positive impact of the experiential learning approach that was used. Students evaluated the usability of SAP ERP as OK, and empirical analysis confirmed that the use of the simulation game for introducing the ERP concepts resulted in anticipated knowledge and skills, while increasing the students’ intent for future engagement.

Different challenges arise while detecting deficient software source code. Usually a large number of potentially problematic entities are identified when an individual software metric or individual quality aspect is used for the identification of deficient program entities. Additionally, a lot of these entities quite often turn out to be false positives, i.e., the metrics indicate poor quality whereas experienced developers do not consider program entities as problematic. The number of entities identified as potentially deficient does not decrease significantly when the identification of deficient entities is carried out by applying code smell detection rules. Moreover, the intersection of entities identified as allegedly deficient among different code smell detection tools is small, which suggests that the implementation of code smell detection rules are not consistent and uniform. To address these challenges, we present a novel approach for identifying deficient entities that is based on applying the majority function on the combination of software metrics. Program entities are assessed according to selected quality aspects that are evaluated with a set of software metrics and corresponding threshold values derived from benchmark data, considering the statistical distributions of software metrics values. The proposed approach was implemented and validated on projects developed in Java, C++ and C#. The validation of the proposed approach was done with expert judgment, where software developers and architects with multiple years of experiences assessed the quality of the software classes. Using a combination of software metrics as the criteria for the identification of deficient source code, the number of potentially deficient object-oriented program entities proved to be reduced. The results show the correctness of quality ratings determined by the proposed identification approach, and most importantly, confirm the absence of false positive entities.

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Software maintenance is one of the key stages in the software lifecycle and it includes a variety of activities that consume the significant portion of the costs of a software project. Previous research suggest that future software maintainability can be predicted, based on various source code aspects, but most of the research focuses on the prediction based on the present state of the code and ignores its history. While taking the history into account in software maintainability prediction seems intuitive, the research empirically testing this has not been done, and is the main goal of this paper. This paper empirically evaluates the contribution of historical measurements of the Chidamber & Kemerer (C&K) software metrics to software maintainability prediction models. The main contribution of the paper is the building of the prediction models with classification and regression trees and random forest learners in iterations by adding historical measurement data extracted from previous releases gradually. The maintainability prediction models were built based on software metric measurements obtained from real-world open-source software projects. The analysis of the results show that an additional amount of historical metric measurements contributes to the maintainability prediction. Additionally, the study evaluates the contribution of individual C&K software metrics on the performance of maintainability prediction models.