arXiv subjects

Guenther Ruhe

Publications and source records attributed to Guenther Ruhe.

21 records · Page 2Linked to original sources

arXiv2017

A Visual Narrative Path from Switching to Resuming a Requirements Engineering Task

Requirements Engineering (RE) is closely tied to other development activities and is at the heart and foundation of every software development process. This makes RE the most data and communication-intensive activity compared to other development tasks. The highly demanding communication makes task switching and interruptions inevitable in RE activities. While task switching often allows us to perform tasks effectively, it imposes a cognitive load and can be detrimental to the primary task, particularly in complex tasks as the ones typical for RE activities. Visualization mechanisms enhanced with analytical methods and interaction techniques help software developers obtain a better cognitive understanding of the complexity of RE decisions, leading to timelier and higher quality decisions. In this paper, we propose to apply interactive visual analytics techniques for managing requirements decisions from various perspectives, including stakeholders communication, RE task switching, and interruptions. We propose a new layered visualization framework that supports the analytical reasoning process of task switching. This framework consists of both data analysis and visualization layers. The visual layers offer interactive knowledge visualization components for managing task interruption decisions at different stages of an interruption (i.e. before, during, and after). The analytical layers provide narrative knowledge about the consequences of task switching decisions and help requirements engineers to recall their reasoning process and decisions upon resuming a task. Moreover, we surveyed 53 software developers to test our visual prototype and to explore more required features for the visual and analytical layers of our framework.

cs.SE↗

arXiv2017

Task Interruptions in Requirements Engineering: Reality versus Perceptions!

Task switching and interruptions are a daily reality in software development projects: developers switch between Requirements Engineering (RE), coding, testing, daily meetings, and other tasks. Task switching may increase productivity through increased information flow and effective time management. However, it might also cause a cognitive load to reorient the primary task, which accounts for the decrease in developers' productivity and increases in errors. This cognitive load is even greater in cases of cognitively demanding tasks as the ones typical for RE activities. In this paper, to compare the reality of task switching in RE with the perception of developers, we conducted two studies: (i) a case study analysis on 5,076 recorded tasks of 19 developers and (ii) a survey of 25 developers. The results of our retrospective analysis show that in ALL of the cases that the disruptiveness of RE interruptions is statistically different from other software development tasks, RE related tasks are more vulnerable to interruptions compared to other task types. Moreover, we found that context switching, the priority of the interrupting task, and the interruption source and timing are key factors that impact RE interruptions. We also provided a set of RE task switching patterns along with recommendations for both practitioners and researchers. While the results of our retrospective analysis show that self-interruptions are more disruptive than external interruptions, developers have different perceptions about the disruptiveness of various sources of interruptions.

cs.SE↗

arXiv2016

Decision Support for Increasing the Efficiency of Crowdsourced Software Development

Crowdsourced software development (CSD) offers a series of specified tasks to a large crowd of trustworthy software workers. Topcoder is a leading platform to manage the whole process of CSD. While increasingly accepted as a realistic option for software development, preliminary analysis on Topcoder's software crowd worker behaviors reveals an alarming task-quitting rate of 82.9%. In addition, a substantial number of tasks do not receive any successful submission. In this paper, we report about a methodology to improve the efficiency of CSD. We apply massive data analytics and machine leaning to (i) perform comparative analysis on alternative technique analysis to predict likelihood of winners and quitters for each task, (ii) significantly reduce the amount of non-succeeding development effort in registered but inappropriate tasks, (iii) identify and rank the most qualified registered workers for each task, and (iv) provide reliable prediction of tasks risky to get any successful submission. Our results and analysis show that Random Forest (RF) based predictive technique performs best among the alternative techniques studied. Applying RF, the tasks recommended to workers can reduce the amount of non-succeeding development effort to a great extent. On average, over a period of 30 days, the savings are 3.5 and 4.6 person-days per registered tasks for experienced resp. unexperienced workers. For the task-related recommendations of workers, we can accurately recommend at least 1 actual winner in the top ranked workers, particularly 94.07% of the time among the top-2 recommended workers for each task. Finally, we can predict, with more than 80% F-measure, the tasks likely not getting any submission, thus triggering timely corrective actions from CSD platforms or task requesters.

cs.SE↗