Non-intrusive monitoring of software quality

Boffoli, Nicola

doi:10.1109/csmr.2006.36

Cited by 5 publications

(5 citation statements)

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“…However, although publications cite development process, measures are, in fact, related to pieces of the process and, thus, can be suitable for SPC. For instance, the measures effort and productivity [Card 1994;Baldassarre et al 2005;Wang and Li 2005;Boffoli 2006;Wang, Qing et al 2006;Chang and Chu 2008;Gou et al 2009;Tarhan and Demirors 2012;Vashisht 2014] are obtained for each task, activity or phase, producing data usefull to describe the process/subprocess performance. Considering that small processes are more suitable for SPC, some measures are related to parts of processes.…”

Section: Discussionmentioning

confidence: 99%

Measures Suitable for SPC: A Systematic Mapping

Barcellos

2016

Anais Do XV Simpósio Brasileiro De Qualidade De Software (SBQS 2016)

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The growing interest of organizations in improving their software processes has lead them to aim at achieving the high maturity, where statistical process control (SPC) is demanded. Through SPC is possible to know processes behavior, predict their performance in future projects and monitor them in order to meet the stablished goals. One of the challenges to perform SPC is the selection of measures suitable for it. Although the literature suggests measures to be used in SPC, information is dispersed. Aiming to provide a consolidated set of measures useful for SPC, as well as the related processes and goals supported by the measures, we conducted a mapping study. This paper presents the study and discusses the main findings.

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Section: Discussionmentioning

confidence: 99%

Measures Suitable for SPC: A Systematic Mapping

Barcellos

2016

Anais Do XV Simpósio Brasileiro De Qualidade De Software (SBQS 2016)

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“…Four of the publications considered relevant to the study describe the same approach applied in different contexts ( [39][40][41] [42]). This approach is the only one identified that deals with knowledge management practice to support process performance analysis.…”

Section: Search Stringmentioning

confidence: 99%

A Knowledge-based Environment for Software Process Performance Analysis

Schots

Magalhães

Gonçalves

et al. 2015

CLEIej

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Background: Process performance analysis is a key step for implementing continuous improvement in software organizations. However, the knowledge to execute such analysis is not trivial and the person responsible to executing it must be provided with appropriate support. Aim: This paper presents a knowledge-based environment, named SPEAKER, proposed for supporting software organizations during the execution of process performance analysis. SPEAKER comprises a body of knowledge and a set of activities and tasks for software process performance analysis along with supporting tools to executing these activities and tasks. Method: We conducted an informal literature reviews and a systematic mapping study, which provided basic requirements for the proposed environment. We implemented the SPEAKER environment integrating supporting tools for the execution of activities and tasks of performance analysis and the knowledge necessary to execute them, in order to meet the variability presented by the characteristics of these activities. Results: In this paper, we describe each SPEAKER module and the individual evaluations of these modules, and also present an example of use comprising how the environment can guide the user through a specific performance analysis activity. Conclusion: Although we only conducted individual evaluations of SPEAKER’s modules, the example of use indicates the feasibility of the proposed environment. Therefore, the environment as a whole will be further evaluated to verify if it attains its goal of assisting in the execution of process performance analysis by non-specialist people.

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“…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%

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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Non-intrusive monitoring of software quality

Cited by 5 publications

References 10 publications

Measures Suitable for SPC: A Systematic Mapping

Measures Suitable for SPC: A Systematic Mapping

A Knowledge-based Environment for Software Process Performance Analysis

Statistical Process Control for Software: Fill the Gap

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