Journal of Software-Evolution and Process最新文献

Requirements elicitation is critical phase of software development that involve direct communication of clients and vendors. In global software development (GSD), importance of requirements elicitation becomes more critical due to geographical distance. The aim of this research is to design and implement requirement elicitation model (REM) where GSD organization can evaluate their organizations and can classify their level of implementation where organization lies. Systematic literature review (SLR) was conducted for the identification of critical success factors (CSFs) from a sample of 70 research articles. Furthermore, the identified CSFs are organized into five levels based on Capability Maturity Model Integration (CMMI) and Software Outsourcing Vendors' Readiness Model (SOVRM). A second SLR was conducted for identifying all possible practices for the implementation of CSFs. To validate the findings of SLR and to find more practices in the view of industrial practitioners for the identified CSFs, a questionnaire survey was conducted in the GSD organizations in which 35 experts, from different countries, contributed. For REM evaluation, we have conducted two case studies. The outcome analysis after conducting case studies confirms that our assessment framework is easier to understand, apply, and successfully critic the strengths and weaknesses of organizations during requirements elicitation in GSD.

Software process simulation models (SPSMs) that are based on descriptive process models offer the executability that can demonstrate dynamic changes of software processes over time. Verification and validation (V&V) is critical in SPSMs for guaranteeing the quality and reliability of models. V&V of dynamic software process models is more complex and challenging than for static software process models. This work systematically summarizes and maps V&V studies in SPSM to provide guidelines for future research and practice. Specifically, this study aims at identifying the focus of research on V&V, the methods used for V&V, and how to implement V&V of SPSMs in software engineering research. We conducted a systematic mapping study on studies of SPSMs that report on their V&V activities. Under the guidance of a V&V meta-model for SPSMs, we study four research questions about V&V process. We identified 107 primary studies from a pool of 313 papers on SPSMs until 2021. There are two main results of our study. The first one presents the relationship between quality aspects of SPSMs and the V&V methods to assure them. The second result reveals the relationships among the modeling process, three modeling steps, five quality aspects, and 10 V&V methods. Generally, researchers do not pay sufficient attention to V&V, as 65.8% ($�(�313�-�107�)�/�313$) failed to mention or elaborate on their V&V process. We systematically summarize and map the state-of-the-art V&V research in software process modeling field to support modelers' practice and improve their V&V process.

Cross-version defect prediction (CVDP) for evolutionary projects has attracted much attention from researchers in recent years. For multiple versions of an object-oriented project, the degree of evolution (e.g., the degree of class change) between successive versions can reflect the differences between versions, which could affect the performance of CVDP. Therefore, how to measure the degree of evolution between successive versions and explore the correlations with the performance of CVDP are very important for software defect prediction. Based on the successive versions of evolutionary projects, this paper proposes six evolutionary measures from three aspects of class change, metric change, and label change, including the Ratio of New Classes (RNC), the Ratio of Deleted Classes (RDC), the Average Ratio of Metric Change (ARMC), the Ratio of Label Changed Classes (RLCC), the Ratio of Unchanged Classes (RUC), and the Ratio of Interference Classes (RIC). An empirical study was conducted on 40 versions of 11 object-oriented projects from the PROMISE repository. Precision, Recall, F-measure, and AUC were used as the performance indicators. Three correlation approaches (Pearson, Spearman, and Kendall) are applied to show the correlations between evolutionary measures and the performance of CVDP. The statistical results show that RNC, RDC, and RUC show no correlation with four performance indicators. ARMC shows weak or medium positive correlations with Recall and F-measure. RLCC and RIC show very strong or strong negative correlations with Recall and F-measure. The results indicate that the correlations between the proposed evolutionary measures and the performance of CVDP are different, which can guide the training set selection of CVDP.

The health of mobile software ecosystems is closely related to the interests of software developers, end-users, and stakeholders. Therefore, it is crucial to maintain the mobile software ecosystem healthy and functioning. Researchers have done considerable research on mobile software ecosystems like Android and iOS. However, the evolution laws implicit in mobile software ecosystems have not attracted widespread attention. This paper proposes a research framework for investigating the evolution process and influencing factors of mobile software ecosystems based on community mining. Firstly, we mine the evolving ecosystem from many mobile software projects based on a community detection algorithm. Then we analyze the evolution process of the ecosystem by identifying evolution events in different periods. Furthermore, we utilize the multinomial logistics regression model to analyze the relevant indicators and summarize the crucial factors affecting the evolution. Meanwhile, by training the long short term memory (LSTM) model to predict evolution events, our prediction accuracy can reach 75%. This work can be used to maintain and improve the healthy operations of mobile software ecosystems.