MétaCan
Menu
Back to cohort
Record W3048392083 · doi:10.1186/s13173-020-00100-8

MylynSDP — Process - aware artifact filtering based on interest

2020· article· en· W3048392083 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of the Brazilian Computer Society · 2020
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsComputer scienceArtifact (error)Software engineeringSoftware developmentSoftwareFunction (biology)Plug-inProcess (computing)Task (project management)Context (archaeology)Software development processGoal-Driven Software Development ProcessProgramming languageArtificial intelligenceSystems engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract A software development process is used by software engineers to guide their activities during all phases of the software product development. When executing a software development process, software engineers may lose time and effort while searching for artifacts or changing contexts. This happens, for example, when they need to search for a specific code file in a list of hundreds of files or when they interrupt an activity to execute another but forget specific details and need to re-execute searches related to the previous activity. This impacts their productivity negatively, because extra time and effort are spent into non-productive work. Therefore, automated assistance is required to mitigate or avoid these issues. The Degree of Interest (DOI) function infers an element’s importance in a context, helping software engineers to handle many artifacts. Mylyn, an Eclipse IDE plugin, uses a DOI function on Java documents to assist programmers when looking for code documents during development. However, Mylyn’s DOI function is limited to the implementation phase of software processes and relies on manual task creation. This paper presents MylynSDP, a software Process-aware extension to Mylyn’s DOI function. MylynSDP’s DOI function infers an artifact’s importance during an activity and filters uninteresting artifacts, reducing the time taken to search items and improving productivity. Mylyn code was augmented, and an evaluation study was performed. Seven subjects executed a software process with many artifacts. Exercise times were recorded for productivity analysis. Subjects answered a Technology Acceptance Model (TAM) questionnaire. New task and artifact creation wizards link tasks and artifacts to specification activities and artifacts, respectively. A new interaction event handles context creation, and the DOI function was extended to other software process phases. Exercise time reduction shows a productivity increase. TAM questionnaire answers show a positive overall willingness to adopt MylynSDP and provide evidence that using a DOI function in different software process phases increases productivity. This work advances the state of the art in software engineering by providing additional methods to support artifact search and discovery, context change management, and artifact relevance mechanisms.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.272
Teacher spread0.239 · 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