The impact of software process improvement on quality: in theory and practice

Ashrafi, Noushin

doi:10.1016/s0378-7206(02)00096-4

Cited by 84 publications

(50 citation statements)

References 21 publications

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“…Current publications (e.g., [24], [25], [26]) present several approaches to measure and ensure quality in processes. For this study, we use a statistical approach that measures the quality of data provided by tools and systems used in these processes (such as bug tracking databases and version control systems).…”

Section: A Measurement Of Process Qualitymentioning

confidence: 99%

When process data quality affects the number of bugs: Correlations in software engineering datasets

Bachmann

Bernstein

2010

2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)

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Software engineering process information extracted from version control systems and bug tracking databases are widely used in empirical software engineering. In prior work, we showed that these data are plagued by quality deficiencies, which vary in its characteristics across projects. In addition, we showed that those deficiencies in the form of bias do impact the results of studies in empirical software engineering. While these findings affect software engineering researchers the impact on practitioners has not yet been substantiated. In this paper we, therefore, explore (i) if the process data quality and characteristics have an influence on the bug fixing process and (ii) if the process quality as measured by the process data has an influence on the product (i.e., software) quality. Specifically, we analyze six Open Source as well as two Closed Source projects and show that process data quality and characteristics have an impact on the bug fixing process: the high rate of empty commit messages in Eclipse, for example, correlates with the bug report quality. We also show that the product quality --measured by number of bugs reported --is affected by process data quality measures. These findings have the potential to prompt practitioners to increase the quality of their software process and its associated data quality. Abstract-Software engineering process information extracted from version control systems and bug tracking databases are widely used in empirical software engineering. In prior work, we showed that these data are plagued by quality deficiencies, which vary in its characteristics across projects. In addition, we showed that those deficiencies in the form of bias do impact the results of studies in empirical software engineering. While these findings affect software engineering researchers the impact on practitioners has not yet been substantiated. In this paper we, therefore, explore (i) if the process data quality and characteristics have an influence on the bug fixing process and (ii) if the process quality as measured by the process data has an influence on the product (i.e., software) quality. Specifically, we analyze six Open Source as well as two Closed Source projects and show that process data quality and characteristics have an impact on the bug fixing process: the high rate of empty commit messages in Eclipse, for example, correlates with the bug report quality. We also show that the product quality -measured by number of bugs reported -is affected by process data quality measures. These findings have the potential to prompt practitioners to increase the quality of their software process and its associated data quality.

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Section: A Measurement Of Process Qualitymentioning

confidence: 99%

When process data quality affects the number of bugs: Correlations in software engineering datasets

Bachmann

Bernstein

2010

2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)

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“…El Emam and Briand (1997) report that many organizations find that the early adoption of certain process areas is more effective than in the recommended sequence. Ashrafi (2003) investigated the impact of SPI methodologies on software quality, specifically CMM and ISO 9000-3. His empirical study indicates that in general process improvement enhances software quality factors but the level of impact is dependent upon the methodology used.…”

Section: Critical Analysis Of Spimentioning

confidence: 99%

The Rosetta Stone Methodology – A Benefits Driven Approach to Software Process Improvement

McLoughlin

Richardson

2010

Product-Focused Software Process Improvement

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Part of the maturity process in the fields of software engineering and information technology has involved, and continues to involve, establishing best practices in the various domains that constitute the area. Specifically, from a software development perspective, there are several competing Software Process Improvement (SPI) methodologies such as the Software Engineering Institute's (SEI) Capability Maturity Model Integrated (CMMI) approach and the International Standards Organization's ISO/IEC 15504 (previously known as SPICE). All these initiatives aim to improve an organization's systems capabilities but they aim to do so from an Information Technology (IT) perspective. However, the majority of organizations are not IT-centric and IT is but one of several elements which contributes to their success. As a result, organizational leaders are more comfortable seeing the benefits of SPI within the context of organizational benefits and objectives and what SPI can contribute to the organization as a whole, not as a purely IT objective. Unfortunately, there is a dearth of methodologies which approach software improvement from an organizational goals and objectives perspective.In response to the lack of an organization-focused approach to SPI, the research presented in this thesis, the Rosetta Stone Methodology, has been developed. This methodology allows organizations to make SPI decisions based on business-and organizational-driven goals and objectives. The methodology allows practitioners to map from a benefits model which is organization-focused to a proven SPI methodology. The methodology itself is fully customizable and allows organizations to make adjustments to the model where they feel it appropriate. Further, in order to demonstrate the usefulness, appropriateness, and practicality of the generic methodology, the Rosetta Stone IGSI-ISM to CMMI Instance mapping (RS-ICMMI) is developed using a generic set of business objectives developed by IBM Consulting Services which are then mapped to an established SPI methodology, namely the CMMI (Staged) model. Using the RS-ICMMI instance of the methodology, practitioners can readily determine which specific Process Areas should be undertaken to achieve specific business objectives. A validation phase was performed whereby both the methodology and the instance mapping were reviewed by experts.

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“…Research shows that the effort put into SPI can assist in producing high quality software, reducing cost and time-to-market, and increasing productivity [2,21,43]. Despite the different advances made in the development of SPI standards and models, the failure rate for SPI programs has been reported up to 70% in a report from the Software Engineering Institute (SEI) [31,47].…”

Section: Research Background and Motivationmentioning

confidence: 99%

Identifying high perceived value practices of CMMI level 2: An empirical study

Niazi

Babar

2009

Information and Software Technology

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a b s t r a c tObjective: In this paper, we present findings from an empirical study that was aimed at identifying the relative ''perceived value" of CMMI level 2 specific practices based on the perceptions and experiences of practitioners of small and medium size companies. The objective of this study is to identify the extent to which a particular CMMI practice is used in order to develop a finer-grained framework, which encompasses the notion of perceived value within specific practices. Method: We used face-to-face questionnaire based survey sessions as the main approach to collecting data from 46 software development practitioners from Malaysia and Vietnam. We asked practitioners to choose and rank CMMI level 2 practices against the five types of assessments (high, medium, low, zero or do not know). From this, we have proposed the notion of 'perceived value' associated with each practice. Results: We have identified three 'requirements management' practices as having a 'high perceived value'. The results also reveal the similarities and differences in the perceptions of Malaysian and Vietnamese practitioners with regard to the relative values of different practices of CMMI level 2 process areas. Conclusions: Small and medium size companies should not be seen as being ''at fault" for not adopting CMMI -instead the Software Process Improvement (SPI) implementation approaches and its transition mechanisms should be improved. We argue that research into ''tailoring" existing process capability maturity models may address some of the issues of small and medium size companies.

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The impact of software process improvement on quality: in theory and practice

Cited by 84 publications

References 21 publications

When process data quality affects the number of bugs: Correlations in software engineering datasets

When process data quality affects the number of bugs: Correlations in software engineering datasets

The Rosetta Stone Methodology – A Benefits Driven Approach to Software Process Improvement

Identifying high perceived value practices of CMMI level 2: An empirical study

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