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Managing Software Process Improvement (SPI) through Statistical Process Control (SPC)

机译：通过统计过程控制（SPC）管理软件过程改进（SPI）

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Measurement based software process improvement is nowadays a mandatory activity. This implies continuous process monitoring in order to predict its behavior, highlight its performance variations and, if necessary, quickly react to them. Process variations are due to common causes or assignable ones. The former are part of the process itself while the latter are due to exceptional events that result in an unstable process behavior and thus in less predictability. Statistical Process Control (SPC) is a statistical based approach able to determine whether a process is stable or not by discriminating between the presence of common cause variation and assignable cause variation. It is a well-established technique, which has shown to be effective in manufacturing processes but not yet in software process contexts. Here experience in using SPC is not mature yet. Therefore a clear understanding of the SPC outcomes still lacks. Although many authors have used it in software, they have not considered the primary differences between manufacturing and software process characteristics. Due to such differences the authors sustain that SPC cannot be adopted "as is" but must be tailored. In this sense, we propose an SPC-based approach that reinterprets SPC, and applies it from a Software Process point of view. The paper validates the approach on industrial project data and shows how it can be successfully used as a decision support tool in software process improvement.
机译：如今，基于测量的软件流程改进已成为一项必不可少的活动。这意味着要进行连续的过程监控，以预测其行为，突出其性能差异，并在必要时对其快速做出反应。流程变化是由于常见原因或可分配原因引起的。前者是过程本身的一部分，而后者则是由于异常事件导致的，这些异常事件导致过程行为不稳定，因此可预测性较差。统计过程控制（SPC）是一种基于统计的方法，能够通过区分常见原因变化和可分配原因变化的存在来确定过程是否稳定。这是一项成熟的技术，已显示在制造过程中有效，但在软件过程环境中尚未奏效。这里使用SPC的经验还不成熟。因此，仍然缺乏对SPC结果的清晰理解。尽管许多作者已在软件中使用它，但他们并未考虑制造和软件过程特征之间的主要差异。由于这种差异，作者坚持认为SPC不能“按原样”采用，而必须进行定制。从这个意义上讲，我们提出了一种基于SPC的方法，该方法可以重新解释SPC，并从软件过程的角度来应用它。本文验证了针对工业项目数据的方法，并展示了如何将其成功用作软件过程改进中的决策支持工具。

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