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Record W1499414128

Auditing for iso 9001 requirements in the context of agile software processes

2012· article· en· W1499414128 on OpenAlexaff
Alain Abran, Malik Qasaimeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsSoftware engineeringCertificationSoftware developmentSoftware development processComputer scienceProcess managementAgile software developmentSoftwareSystems engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

ISO 9001 demands of (software) organizations that a rigorous demonstration of their software processes be implemented and a set of guidelines followed at various levels of abstraction. What these organizations need to show, in other words, is that their software processes have been designed and implemented in a way that allows for a level of configuration and operation that complies with ISO 9001 requirements. For software organizations needing ISO 9001 certification, it is important that they establish a software process life cycle that can manage the requirements imposed by this certification standard. However, software organizations that develop their software products using the agile software processes, such as Extreme Programming (agile-XP), face a number of challenges in their effort to demonstrate that their process activities conform to ISO 9001 requirements, major ones being: product construction, traceability, and measurement. Agile software organizations must provide evidence of ISO 9001 conformity, and they need to develop their own procedures, tools, and methodologies to do so. As yet, there is no consensus on how to audit the agile software organization to ensure that their software processes have been designed and implemented in conformity with ISO 9001 requirements. Moreover, it is challenging to ensure that such lightweight documentation methodologies meet these requirements for certification purposes. The motivation of this research is to help software organizations that use agile software processes in their effort to meet the ISO 9001 certification requirements. This research project is also aimed at helping IS auditors extract auditing evidence that demonstrates conformity to the ISO 9001 requirements that must be met by agile software organizations. Extreme programming (agile-XP) has been selected for improvement as a candidate agile process. This selection was based on the literature indicating a higher adoption of agile-XP over other agile software processes. The goal of this research project is to improve the ability of the agile-XP process to meet the auditing requirements of ISO 9001. The goal of the research also focuses on helping agile software organizations in their effort to become ISO 9001 certified. The main objective of this research project is to design an auditing model that covers the measurement and traceability requirements of ISO 9001. The auditing model should provide IS auditors with auditing evidence that the software projects developed with the agile-XP process have fulfilled the requirements of ISO 9001. The objective also proposes several sub processes to enhance the early planning activities of agile-XP according to ISO 9001 requirements. To achieve these objectives, the main phases of the research methodology are: Investigation of the capability of agile-XP to achieve the requirements of ISO 9001 software process certification; modification of the early phases of agile-XP (i.e. release planning phase) using CMMI-DEV; and design of an auditing model for ISO 9001 traceability and measurement requirements. The main outcome of this research study, which is an auditing model that is aligned with the principles of agile-XP and focuses on ISO 9001 traceability and measurement requirements to provide the IS auditors with a methodological approach for the auditing process. The auditing model has been assessed based on case studies selected from the literature.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.768
Threshold uncertainty score0.176

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.315
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

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