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

Baldassarre, María Teresa; Boffoli, Nicola; Caivano, Danilo; Visaggio, Giuseppe

doi:10.1007/978-3-540-24659-6_3

Cited by 25 publications

(20 citation statements)

References 14 publications

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“…It doesn't support the manager during the causes investigation and the selection of the appropriate corrective actions. This solution extends the SPC-theory by providing a specific interpretation (Table 2) of the anomaly for each run test failure (section 3.2) from the software process point of view, and suggesting possible causes that make the process "Out of Control" (Baldassarre, 2004). More precisely, the authors have arranged and interpreted the selected SPC indicators (Table 1) in logical classes: sigma (RT1, RT2, RT3), limit (RT4, RT5, RT6) and trend (RT7, RT8), for details refer to (Baldassarre, 2004).…”

Section: Causes Investigationmentioning

confidence: 68%

“…recalibrate control limits) according to performance changes. Point (1) follows from the experience acquired during empirical validation of the SPC approach in a previous study (Baldassarre et al, 2004). Following to this experience we have generalized a set of relations between "what happens" in the process and what the best actions to undertake are (Table 3).…”

Section: Tuning Sensibilitymentioning

confidence: 99%

“…In each case, not only are there few cases of successful applications but they don't clearly illustrate the meaning of control charts and related indicators in the context of software process application. Given the above considerations, the aim of this work is to generalize and put together the experiences collected by the authors in previous studies on the use of Statistical Process Control in the software context (Baldassarre et al, 2004;Baldassarre et al, 2005;Caivano 2005;Boffoli, 2006;Baldassarre et al, 2008;Baldassarre et al, 2009) and present the resulting stepwise approach that: starting from stability tests, known in literature, selects the most suitable ones for software processes (tests set), reinterprets them from a software process perspective (tests interpretation) and suggest a recalculation strategy for tuning the SPC control limits. The paper is organized as follows: section 2 briefly presents SPC concepts and its peculiarities; section 3 discusses the main differences and lacks of SPC for software and presents the approach proposed by the authors; finally, in section 4 conclusions are drawn.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Process Control for Software: Fill the Gap

Boffoli¹,

Baldassarre²,

Caivano³

2010

Quality Management and Six Sigma

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The characteristic of software processes, unlike manufacturing ones, is that they have a very\ud high human-centered component and are primarily based on cognitive activities. As so, each\ud time a software process is executed, inputs and outputs may vary, as well as the process\ud performances. This phenomena is better identified in literature with the terminology of\ud “Process Diversity” (IEEE, 2000). Given the characteristics of a software process, its intrinsic\ud diversity implies the difficulty to predict, monitor and improve it, unlike what happens in\ud other contexts. In spite of the previous observations, Software Process Improvement (SPI) is a\ud very important activity that cannot be neglected. To face these problems, the software\ud engineering community stresses the use of measurement based approaches such as QIP/GQM\ud (Basili et al., 1994) and time series analysis: the first approach is usually used to determine\ud what improvement is needed; the time series analysis is adopted to monitor process\ud performances. As so, it supports decision making in terms of when the process should be\ud improved, and provides a manner to verify the effectiveness of the improvement itself.\ud A technique for time series analysis, well-established in literature, which has given\ud insightful results in the manufacturing contexts, although not yet in software process ones is\ud known as Statistical Process Control (SPC) (Shewhart, 1980; Shewhart, 1986). The technique\ud was originally developed by Shewhart in the 1920s and then used in many other contexts.\ud The basic idea it relies on consists in the use of so called “control charts” together with their\ud indicators, called run tests, to: establish operational limits for acceptable process variation;\ud monitor and evaluate process performances evolution in time. In general, process\ud performance variations are mainly due to two types of causes classified as follows:\ud  Common cause variations: the result of normal interactions of people, machines,\ud environment, techniques used and so on.\ud  Assignable cause variations: arise from events that are not part of the process and\ud make it unstable.\ud In this sense, the statistically based approach, SPC, helps determine if a process is stable or\ud not by discriminating between common cause variation and assignable cause variation. We\ud can classify a process as “stable” or “under control” if only common causes occur. More\ud precisely, in SPC data points representing measures of process performances are collected.\ud These values are then compared to the values of central tendency, upper and lower limit of\ud admissible performance variations.\ud While SPC is a well established technique in manufacturing contexts, there are only few\ud works in literature (Card, 1994; Florac et al., 2000; Weller, 2000(a); Weller, 2000(b); Florence,\ud 2001; Sargut & Demirors, 2006; Weller, & Card. 2008; Raczynski & Curtis, 2008) that present\ud successful outcomes of SPC adoption to software. In each case, not ...

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Section: Causes Investigationmentioning

confidence: 68%

Section: Tuning Sensibilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical Process Control for Software: Fill the Gap

Boffoli¹,

Baldassarre²,

Caivano³

2010

Quality Management and Six Sigma

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“…In the context of control and monitoring there are no known methods to establish criteria for systematic monitoring, so the following problems (Baldassarre, Boffoli, Caivano, & Visaggio, 2004) are evident: monitoring involves the need to define the appropriate thresholds and use them to evaluate the performance of what is expected, considering that you cannot set limits of universal validity due to the heterogeneity of the operating environments and the large number of monitored processes; identifying abnormal behavior and promptly react to this, so it is necessary to understand what is abnormal behavior and how it can be identified; and determining how to adjust the sensitivity of monitoring in the light of continuous change of the behavior of a process that quickly reflects the variation according to the observed phenomenon or process anomalies, monitoring criterion, and implemenevidenciar varios problemas y formular las preguntas. El propósito de las preguntas es identificar, dentro de la literatura, las obras más relevantes que se han centrado en el control estadístico de procesos [SPC] para la gestión cuantitativa en Ingeniería de Software.…”

Section: Formulación De Preguntasmentioning

confidence: 99%

“…En el contexto del control y monitoreo son desconocidos métodos que establezcan criterios de seguimiento sistemático, por lo que se evidencian los siguientes problemas (Baldassarre, Boffoli, Caivano, & Visaggio, 2004): el monitoreo implica la necesidad de definir los umbrales apropiados y usarlos para evaluar el desempeño de lo que se espera, considerando que no se pueden establecer límites de validez universal debido a la heterogeneidad de los entornos operativos y al considerable número de procesos monitoreados; la necesidad de identificar un comportamiento anormal y reaccionar oportunamente ante este, por lo cual se requiere entender lo que es un comportamiento anormal y la forma en que se pueden identificar; y la manera de ajustar la sensibilidad del monitoreo a la luz del continuo cambio del comportamiento de un proceso que refleje velozmente la variación en función del fenómeno observado o las anomalías en el proceso, los criterios de monitoreo y el método de implementación, para articular SPC a la gestión y, si es posible, aplicar SPC a empresas emergentes. En este tation method, in order to articulate SPC to the management and, if possible, apply SPC to emerging companies.…”

Section: Formulación De Preguntasunclassified

Statistical Control of Software Process: A Systematic Review

Garcés

Pino

2014

sist.telemat.

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The Software Processes Improvement requires advanced techniques for the quantitative management process, so that from the analysis of the relevant indicators in the organization, actions can be taken to facilitate the decision-making process in favor of this improvement. Among the set of techniques, special attention is devoted to the Statistical Process Control (SPC), which has gained acceptance in enterprises preparing to attain high degrees of maturity in their processes, and require the establishment of measuring programs. SPC is useful in software development for several reasons. Control Charts help identify process shifts and abnormal variations. It is therefore pertinent to assess how successful SPC is in the context of software production. In this sense, we have not found any integrated proposal of quantitative management applied to distributed software development, processes that allow the attainment of important benefits, and, at the same time, that more organizations are improving their processes. These considerations motivate the need to define and carry out a Systematic Review to assess whether SPC is being used effectively and correctly, and to determine the main obstacles to a successful application of SPC in SPI efforts. Thus, the systematic review allows developing a rigorous analysis of the current state of the SPC, which is a starting point to address how it can be successfully used as a decision-making support tool in software-process improvement in agreement with the available empirical evidence reported in the literature. KEYWORDS Statistical Process Control, Six Sigma, Software Process, Systematic ReviewControl estadístico de procesos en software: una revisión sistemática RESUMEN La mejora de procesos software [Software Process Improvement, SPI] requiere de técnicas avanzadas para la gestión cuantitativa, de modo que, a partir del análisis de los indicadores relevantes en la organización, se pueda facilitar la toma de decisiones para dicha mejora. Dentro del conjunto de técnicas merece especial atención el control estadístico de procesos (Statistical Process Control, SPC) que ha ido ganando aceptación en las empresas que se preparan para alcanzar altos grados de madurez en sus procesos y que lo requieren para la implementación de sus programas de medición. El artículo desarrolla una revisión sistemática para un análisis crítico del estado del arte sobre las técnicas del SPC más adecuadas que se pueden adoptar en la gestión cuantitativa de software. En este sentido, al no encontrar una propuesta integrada de gestión cuantitativa software para procesos distribuidos que permita beneficios importantes a medida que más organizaciones mejoraran sus procesos o a los gestores de proyectos de software, la revisión sistemática es el modo riguroso para evaluar y determinar herramientas, tecnologías, técnicas y métodos de aplicación de acuerdo con la evidencia empírica disponible reportada en la literatura.PALABRAS CLAVE Control estadístico de procesos; Six Sigma; procesos de software; mejora...

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