Practical Software Measurement: Measuring for Process Management and Improvement,

Florac, William A.; Park, Robert E.; Carleton, Anita D.

doi:10.21236/ada325551

Cited by 73 publications

(31 citation statements)

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“…It is also important to emphasize that process improvement is an integral responsibility of software process management (Florac et al 1997). Consequently, we believe that it is important to work on how to establish a basic process management infrastructure that is tailored to SMEs and which will support the key principles of process management (to define, to measure, to control and to continually improve the processes).…”

Section: Models Usedmentioning

confidence: 99%

Software process improvement in small and medium software enterprises: a systematic review

2007

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Small and medium enterprises are a very important cog in the gears of the world economy. The software industry in most countries is composed of an industrial scheme that is made up mainly of small and medium software enterprises-SMEs. To strengthen these types of organizations, efficient Software Engineering practices are needed-practices which have been adapted to their size and type of business. Over the last two decades, the Software Engineering community has expressed special interest in software process improvement (SPI) in an effort to increase software product quality, as well as the productivity of software development. However, there is a widespread tendency to make a point of stressing that the success of SPI is only possible for large companies. In this article, a systematic review of published case studies on the SPI efforts carried out in SMEs is presented. Its objective is to analyse the existing approaches towards SPI which focus on SMEs and which report a case study carried out in industry. A further objective is that of discussing the significant issues related to this area of knowledge, and to provide an up-to-date state of the art, from which innovative research activities can be thought of and planned.

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Section: Models Usedmentioning

confidence: 99%

Software process improvement in small and medium software enterprises: a systematic review

2007

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“…an assignable cause is assumed to be present. For completeness and clearness it is the case to point out that the first 4 tests among those that follow are also referred to as "detection rules" and are the most (and often the only ones) used tests (Wheeler & Chambers, 1992;Florac et al, 1997) within the software engineering community.…”

Section: Rt6mentioning

confidence: 99%

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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“…Some of these studies are based on process improvement models and focus on SPC as a way of achieving high process maturities (Burr and Owen, 1996;Florac and Carleton, 1999;Carleton and Florac, September 1999;Florac et al, 2000;Florac et al, 1997;Jakolte and Saxena, 2002;Humphrey, 1989). These model-based studies mostly represent CMM understanding of SPC for software process improvement.…”

Section: Utilization Of Spc In Software Developmentmentioning

confidence: 99%

Utilization of statistical process control (SPC) in emergent software organizations: Pitfalls and suggestions

Sargut

Demirörs

2006

Software Qual J

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Common wisdom in the domain of software engineering tells us that companies should be mature enough to apply Statistical Process Control (SPC) techniques. Since reaching high maturity levels (in CMM or similar models such as ISO 15504) usually takes 5-10 years, should software companies wait years to utilize Statistical Process Control techniques? To answer this question, we performed a case study of the application of SPC techniques using existing measurement data in an emergent software organization. Specifically, defect density, rework percentage and inspection performance metrics are analyzed. This paper provides a practical insight on the usability of SPC for the selected metrics in the specific processes and describes our observations on the difficulties and the benefits of applying SPC to an emergent software organization.

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Practical Software Measurement: Measuring for Process Management and Improvement,

Cited by 73 publications

References 1 publication

Software process improvement in small and medium software enterprises: a systematic review

Software process improvement in small and medium software enterprises: a systematic review

Statistical Process Control for Software: Fill the Gap

Utilization of statistical process control (SPC) in emergent software organizations: Pitfalls and suggestions

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