Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
ESEML: Empirical Softare Engineering Modeling Language
Search
Bruno Cartaxo
October 22, 2012
Research
0
58
ESEML: Empirical Softare Engineering Modeling Language
Bruno Cartaxo
October 22, 2012
Tweet
Share
More Decks by Bruno Cartaxo
See All by Bruno Cartaxo
Mestrado e Doutorado em Computação - UTFPR
brunocartaxo
0
16
Mestrado e Doutorado - Tempest
brunocartaxo
0
19
MESTRADO E DOUTORADO: Que danado é isso? Devo fazer?
brunocartaxo
0
66
RAPID REVIEW IN SOFTWARE ENGINEERING: Making Scientific Evidence Relevant To Practitioners
brunocartaxo
0
40
Rapid Reviews in Software Engineering
brunocartaxo
0
65
DO BRASIL À CHINA, PASSANDO POR CINCO CONTINENTES: COMO PESQUISAR ME ABRIU AS PORTAS PARA O MUNDO
brunocartaxo
0
42
SOFTWARE ENGINEERING RESEARCH COMMUNITY VIEWPOINTS ON RAPID REVIEWS
brunocartaxo
1
150
KNOWLEDGE AND TECHNOLOGY TRANSFER BETWEEN RESEARCH AND PRACTICE IN SOFTWARE ENGINEERING - LINE OF RESEARCH AND VISIONS
brunocartaxo
1
54
O Impacto das Novas Tecnologias na Prática Jurídica
brunocartaxo
0
140
Other Decks in Research
See All in Research
「Goトレ」のご紹介
smartfukushilab1
0
210
LayerXにおけるAI・機械学習技術の活用と展望 / layerx-ai-jsai2024
shimacos
2
2.5k
仮説検定とP値
shuntaros
6
7.3k
-SSII技術マップを通して見る過去・現在,そして未来-
hf149
1
490
SSII2024 [OS2] 大規模言語モデルとVision & Languageのこれから
ssii
PRO
5
1.3k
SSII2024 [TS3] 画像認識におけるマルチモーダル基盤モデル ~基盤モデル、あなたのタスクに役立つかも?~
ssii
PRO
0
810
LINEチャットボット「全力肯定彼氏くん(LuC4)」の 1年を振り返る
o_ob
0
680
Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction(日本語版)
aiueola
0
120
大規模言語モデル (LLM) の技術と最新動向
ikuyamada
30
15k
機械学習と最適化の融合動的ロットサイズ決定問題を例として
mickey_kubo
2
360
SSII2024 [TS1] 生成AIと3次元ビジョン ~3次元生成AIの最先端の理論~
ssii
PRO
1
980
第28回 著者ゼミ:Identification of drug responsible glycogene signature in liver carcinoma from meta-analysis using RNA-seq data
ktatsuya
2
200
Featured
See All Featured
The Language of Interfaces
destraynor
151
23k
The Mythical Team-Month
searls
217
43k
How to Ace a Technical Interview
jacobian
274
23k
Building a Modern Day E-commerce SEO Strategy
aleyda
25
6.7k
Building Effective Engineering Teams - LeadDev
addyosmani
47
2.2k
The Straight Up "How To Draw Better" Workshop
denniskardys
229
130k
Scaling GitHub
holman
458
140k
For a Future-Friendly Web
brad_frost
173
9.2k
Git: the NoSQL Database
bkeepers
PRO
423
64k
Fantastic passwords and where to find them - at NoRuKo
philnash
42
2.7k
Build The Right Thing And Hit Your Dates
maggiecrowley
28
2.2k
Pencils Down: Stop Designing & Start Developing
hursman
118
11k
Transcript
ESEML Empirical Softare Engineering Modeling Language Bruno Cartaxo [
[email protected]
] Ítalo
Costa [
[email protected]
] Dhiego Martns [
[email protected]
] André Santos [
[email protected]
] Sérgio Soares [
[email protected]
] Vinícius Garcia [
[email protected]
]
MOTIVATION Researches in Softare Engineering normally proposes net practces to
increase productvity and quality. A great part of these researches fail to present empirical evidence.
EMPIRICAL SOFTWARE ENGINEERING There are several types of empirical studies.
Such as, surveys, case studies, secondary studies, acton research and controlled experiments.
CONTROLLED EXPERIMENTS According to Sjoberg only 1.9% of artcles has
a controlled experiment and the quality is not very high. With Experiment s Without Experiment
CONTROLLED EXPERIMENTS Wide range of skills is necessary to conduct
experiments that ofen create a barrier for adoptng it. Skills in terminology, statstcs knot hot and expertse in experimental design.
OBJECTIVE Facilitate the modeling process and defniton of an experimental
plan. By mitgatng social barriers betteen stakeholders. Such as statstcians, experiments designers, and domain experts.
PROPOSAL DSLs are efcient to model specifc domains + Controlled
experiments have their specifc domain elements = ESEML guides controlled experiments modeling in softare engineering and reduces social barriers
ESEML A visual DSL for modeling controlled experiments in softare
engineering. That Automatcally generate the experimental plan from an instantaton of a domain model.
METHODOLOGY Informal reviet of models, ontologies and formal representatons for
controlled experiments. Meta-model based on the reviet. Microsof DSL Tools to create the DSL and its related torkbench.
META-MODEL
LANGUAGE WORKBENCH ELEMENTS PALLETE EXPERIMENT MODEL
LANGUAGE WORKBENCH Parameter Hypothesis Dependent Variable Tratment Factor Experiment Validity
Goal Queston Metric
GENERATED DOCUMENT
DOCUMENT PARTS
CONCLUSION ESEML is the kickof to a major initatve for
defne a platorm of empirical studies in softare engineering.
FUTURE WORK Automatcally generaton of artfacts to collect data and
execute experiments. Systematc reviet to more accurate meta-model . Empirical evaluaton of ESEML.
?