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Goal-oriented modeling languages have been proposed to elicit, analyze, and document high-level system requirements in the early stages of the requirements engineering (RE) process. Problems during this stage may disseminate to the subsequent stages in the software development process and artifacts. Therefore, improving the quality of goal models would improve the quality of the requirements and, consequently, the quality of the developed system. This paper proposes an approach to help modelers identify quality improvement opportunities in Goal-oriented Requirements Language (GRL) goal models. To this end, a list of GRL bad smells (i.e., bad quality symptoms) is introduced and evaluated by experts. Then, an automated rule-based technique is proposed to detect the instances of these smells. The proposed approach is evaluated using a dataset gathered from academic and real-world projects. The results show that the developed technique could successfully detect the instances of the proposed bad smells in the evaluation models. We also found that the instances of the proposed bad smells were prevalent in both academic and industrial settings. The proposed bad smells and the detection technique provide a tool to locate quality improvement opportunities in GRL goal models. [ABSTRACT FROM AUTHOR]

We offer a novel test of whether non-binding goals set ahead of a task are effective motivators, taking into account that individuals in principle could easily revise these goals. In our setting, subjects either set a goal some days prior to an online task (early goal) or right at the start of the task (late goal). Two further treatments allow for (unanticipated) explicit revision of the early goal. We observe that (i) early goals are larger than late goals; (ii) subjects who set early goals work more than those who only set a late goal if they explicitly revise their goal and are reminded about their revised goal. A secondary contribution of our paper is that our design addresses a treatment migration problem present in earlier studies on goals that stems from the fact that subjects in a 'no goals' control condition may privately set goals. [ABSTRACT FROM AUTHOR]