On the Relationship between Inspection and Evolution in Software Product Lines: An Exploratory Study

Iuri Santos Souza, R. Oliveira, G. Gomes, E. Almeida
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引用次数: 3

Abstract

Manage the evolution in Software Product Lines (SPL) can bring some benefits such as keep the trace ability between assets in core assets and products, avoid some irregular growth or decrease before it becomes a threat to the system, and also use the products feedback to improve the core asset quality. In order to understand the evolution in SPL, this paper presents an empirical study to investigate evidence between information from features non-conformities and data from corrective maintenance, based on an SPL industrial project in the medical domain. The investigation aims at tracking the features non-conformities and their likely root causes using results from two preliminary studies. The first one captured and classified the features non-conformities from features specification of nine sub-domains and the second one investigated the evolution of SPL assets along the sub-domains development. The study sample was analyzed using statistical techniques, such as Spearman correlation rank and Poisson regression models. The findings indicated that there is significant positive correlation between feature non-conformities and corrective maintenance. Sub-domains with a high number of feature non-conformities had a higher number of corrective maintenance. Moreover, sub-domains qualified as high risk have also positive correlation with corrective maintenance. This correlation allows the building of predictive models to estimate corrective maintenance based on the risk sub-domain attribute values.
软件产品线中检验与演进关系的探索性研究
对软件产品线(Software Product Lines, SPL)的演进进行管理,可以保持核心资产和产品中资产之间的可追溯性,避免一些不规律的增长或减少,避免其对系统造成威胁,还可以利用产品的反馈来提高核心资产的质量。为了了解SPL的演变,本文基于一个医疗领域的SPL工业项目,对特征不符合信息与纠正性维护数据之间的证据进行了实证研究。调查的目的是利用两项初步研究的结果跟踪不符合项的特征及其可能的根本原因。第一部分从九个子域的特征规范中捕获并分类了特征不一致性;第二部分研究了SPL资产在子域发展过程中的演化。使用Spearman相关秩和泊松回归模型等统计技术对研究样本进行分析。结果表明,特征不符合与纠正性维修之间存在显著的正相关关系。具有大量特征不符合的子域具有更高数量的纠正维护。此外,被认定为高风险的子域也与纠正性维护呈正相关。这种相关性允许基于风险子域属性值构建预测模型来估计纠正性维护。
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