FUZZY DEFINITION FOR TASKS AND RESOURCES CATEGORIZATION IN SOFTWARE PROJECTS

W. Ajayi
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Abstract

: Risk in software development process is an event that if allowed to occur can cause a major hazard in the development process and subsequently, deterioration in software quality. Risk reduction has been a major concern to software project stakeholders. A major way of preventing risks is to get the tasks and resource combination / allocation right from onset in software development. Otherwise, it could lead to major bottleneck; one that can either halt project temporarily, extending delivery date or cause a total stoppage of the project. To forestall such occurrence, this work looked into tasks and resources combination for software projects by remodeling the risk identification procedure of an existing risk model (the Riskit model)- introduced by Kontio and Basili. A typical project process to depict the risk identification procedure of the original riskit model was arranged, the risk identification stage was then redesigned and broken down into different parts; the total RAG time (RAG T ) was generated in the process. The main task-resource fuzzification then took place in Part C and project information update took place in Part D of the system. Pointer setting to risky task was estimated from the difficulty level of the task-hours (X t ) and the required resources (X h ) and categorized using trapezoidal membership function in fuzzy logic. The resulting system later named ERAM was compared with Brainstorming and Riskit Analysis Graph technique (BRAG) using runtime and task segments performed by both models. Applying the task –resource combination, the result showed that the original model BRAG took 88 minutes while ERAM generated a faster advisory output in 3 seconds with X t X h coordinate W between 0.9 -1.0. Again, the study revealed that time saving is not the only benefit if resource (X h ) and Task (or task hour (X t )) combination and categorization is properly done. BRAG yearly runtime estimate was 237 hours at overhead cost of ₦3,051,612 while ERAM was 2.6 hours at overhead cost of ₦37,078. The study concluded that ERAM is adequate in speedy classification of software project constituents, early risk identification, reduction in delivery time and cost. Therefore, it is recommended that software risks experts and project managers adopt ERAM to increase speed in risk identification, gain ample time for other project activities and reduced overhead cost .
软件项目中任务和资源分类的模糊定义
软件开发过程中的风险是一种事件,如果允许发生,可能会导致开发过程中的主要危害,并随后导致软件质量的恶化。降低风险一直是软件项目涉众关注的主要问题。预防风险的一个主要方法是从软件开发开始就正确地获得任务和资源组合/分配。否则,可能会导致重大瓶颈;一种可以使项目暂时停止,延长交付日期或导致项目完全停止的情况。为了防止这种情况的发生,本工作通过重构由Kontio和Basili引入的现有风险模型(Riskit模型)的风险识别过程,研究了软件项目的任务和资源组合。安排了一个典型的项目过程来描述原始风险模型的风险识别过程,然后对风险识别阶段进行了重新设计,并将其分解为不同的部分;在此过程中产生的总RAG时间(RAG T)。然后在系统的C部分进行主要任务-资源模糊化,在D部分进行项目信息更新。从任务小时(X t)和所需资源(X h)的难度等级估计风险任务的指针设置,并利用模糊逻辑中的梯形隶属函数进行分类。最终的系统后来被命名为ERAM,使用两个模型执行的运行时和任务段,将其与头脑风暴和风险分析图技术(BRAG)进行比较。应用任务-资源组合,结果表明,原始模型自夸耗时88分钟,而ERAM在3秒内生成更快的咨询输出,X t X h坐标W在0.9 -1.0之间。同样,研究表明,如果资源(X小时)和任务(或任务小时(X小时))组合和分类得当,节省时间并不是唯一的好处。自夸的年运行时间估计为237小时,管理成本为3,051,612奈拉,而ERAM的年运行时间估计为2.6小时,管理成本为3,7078奈拉。研究得出结论,ERAM在软件项目成分的快速分类、早期风险识别、减少交付时间和成本方面是足够的。因此,建议软件风险专家和项目经理采用ERAM来提高风险识别的速度,为其他项目活动赢得充足的时间,并减少间接成本。
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