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Record W2045837594 · doi:10.1109/icisa.2013.6579470

Comprehensive Integrated Checklists for Requirements Engineering and Software Project Management

2013· article· en· W2045837594 on OpenAlexaff
Husam Suleiman, Adedamola Adepetu, Edin Arnautović, Davor Svetinović

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSoftware project managementSoftware developmentComputer scienceSoftware engineeringSoftware requirementsSoftware Engineering Process GroupSoftware development processPersonal software processChecklistSoftware peer reviewSocial software engineeringSystems engineeringEngineering managementSoftware constructionProcess managementSoftwareEngineering

Abstract

fetched live from OpenAlex

Software engineering processes are often challenged by overlooked tasks and misguided decisions that are simple but yet essential for the success of the system to be developed. A list of guidelines and steps is required to help facilitate software development processes by efficiently guiding engineers and managers at different stages of software development. Moreover, the ensuring of the execution of required tasks is essential to reduce the likelihood of the system failure. In other critical areas such as medical surgery or aerospace control, the use of checklists has proven as a successful practice to ensure the effective and efficient execution of the tasks in a process. Although several of the current practices in software development include using some art of checklists to monitor the software development processes, there are no unified and integrated checklists from research and industry resulting in significant disparities. Such ambiguity could be even counter-productive instead of easing the software development processes. In order to fill this gap, we developed comprehensive and integrated software engineering checklists for the critical areas of requirements engineering and software project management. To create such comprehensive checklists and ensure their completeness, we applied the systematic literature review method. We analyzed 323 documents from academia and industry, and identified a total of 183 requirements engineering checklist items and 263 project management checklist items.

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)0.000
metaresearch head score (Gemma)0.000
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: Methods
Teacher disagreement score0.722
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.028
GPT teacher head0.275
Teacher spread0.247 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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