While there is a growing number of studies investigating the determinants of social media addiction, there is a lack of research on examining the importance of such predictors and their inter-correlations and interdependences from psychotherapists' and researchers' point of views. Hence, utilizing the "Decision Making Trial and Evaluation Laboratory (DEMATEL)" technique, the current study investigated the clinical importance of social media addiction from the perspective of researchers and psychotherapists. Accordingly, by reviewing the literature 15 distal predictors of social media addiction were extracted and further classified into three groups of personality factors, comorbid symptoms, and psychosocial factors. From the data collected from 35 respondents, the results highlighted the group of personality factors as the most important dimension increasing the risk for developing social media addiction from the respondents' perspective. Moreover, the DEMATEL results revealed the predictors of openness to experience (personality dimension), loneliness (psychosocial), and depression (comorbid) as the most important predictors of social media addiction within each group. The results and implications of the study are discussed.

It is believed that the effectiveness of requirements engineering activities depends at least partially on the individuals involved. One of the factors that seems to influence an individual's effectiveness in requirements engineering activities is knowledge of the problem being solved, i.e., domain knowledge. While a requirements engineer's having in-depth domain knowledge helps him or her to understand the problem easier, he or she can fall for tacit assumptions of the domain and might overlook issues that are obvious to domain experts. This paper describes a controlled experiment to test the hypothesis that adding to a requirements elicitation team for a computerbased system in a particular domain, requirements analysts that are ignorant of the domain improves the effectiveness of the requirements elicitation team. The results, although not conclusive, show some support for accepting the hypothesis. The results were analyzed also to determine the effect of creativity, industrial experience, and requirements engineering experience. The results suggest other hypotheses to be studied in the future.

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Telematics and Informatics (2020). In press.

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Analogous to Computer-Aided Software Engineering (CASE), which aims to facilitate Software Engineering through specialized tools, Computer-Aided Method Engineering (CAME) strives to support a wide range of activities carried out by method engineers. Although there is consensus on the importance of tool support in method engineering, existing CAME environments are incomplete prototypes, each covering just a few steps of the method engineering process. This paper summarizes the history and the state of the practice in CAME technology, and provides criteria-based critique on existing CAME environments, thus highlighting their strengths and weaknesses.

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HighlightsWe used a genetic algorithm (GA) in combination with C4.5 decision tree for prediction of miRNA targets.The GA-based decision tree was used for selection of the best rules out of the rule sets.We used a good measure (weighted F-measure) as fitness function.Our method extracted 7 rules, which can be implemented for other similar dataset to prediction of miRNAs target.We achieved 93.9% and 97.1% accuracy of classification for two previously published datasets.