Towards just-in-time refactoring recommenders

Pantiuchina, Jevgenija; Bavota, Gabriele; Tufano, Michele; Poshyvanyk, Denys

doi:10.1145/3196321.3196365

Cited by 13 publications

(13 citation statements)

References 8 publications

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“…Motivation. An example that is similar to this study is the work by Pantiuchina et al [17] that proposes to predict code smells. Their work uses source code quality to predict whether a module is likely to be affected by code smells in the future.…”

Section: Rq 3 : Can We Use An Existing Approach To Predictsupporting

confidence: 62%

“…This technique is widely used for defect prediction, where the characteristics of each module are used to predict whether the module is defective [16]. Since an example that is similar to this study is the work by Pantiuchina et al [17] that proposes to predict code smells, we can use it as a baseline approach.…”

Section: Predicting Decaying Modulesmentioning

confidence: 99%

“…Baseline. We set a baseline model inspired by the work by Pantiuchina et al that was proposed to predict modules that will become smelly [17]. The left part of Fig.…”

Section: Predicting Decaying Modulesmentioning

confidence: 99%

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Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction

Sae-Lim

Hayashi

Saeki

2021

IEICE Trans. Inf. & Syst.

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Code smells can be detected using tools such as a static analyzer that detects code smells based on source code metrics. Developers perform refactoring activities based on the result of such detection tools to improve source code quality. However, such an approach can be considered as reactive refactoring, i.e., developers react to code smells after they occur. This means that developers first suffer the effects of low-quality source code before they start solving code smells. In this study, we focus on proactive refactoring, i.e., refactoring source code before it becomes smelly. This approach would allow developers to maintain source code quality without having to suffer the impact of code smells. To support the proactive refactoring process, we propose a technique to detect decaying modules, which are non-smelly modules that are about to become smelly. We present empirical studies on open source projects with the aim of studying the characteristics of decaying modules. Additionally, to facilitate developers in the refactoring planning process, we perform a study on using a machine learning technique to predict decaying modules and report a factor that contributes most to the performance of the model under consideration.

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Section: Rq 3 : Can We Use An Existing Approach To Predictsupporting

confidence: 62%

Section: Predicting Decaying Modulesmentioning

confidence: 99%

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Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction

Sae-Lim

Hayashi

Saeki

2021

IEICE Trans. Inf. & Syst.

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“…Pošto ne postoji mnogo rešenja koja koriste istoriju promena koda za detekciju code smell-ova, razmotrena su i rešenja koja koriste sličan pristup u cilju detekcije drugih vrsta nedostataka u kodu. U radu [9] su autori predstavili alat za prevenciju code smell-ova COSP (COde Smell Predictor) koji za datu klasu predviđa da li će sadržati neki tip code smell-a u narednih t dana. Za implementaciju COSP alata su koristili Weka 1 implementaciju Random Forest klasifikatora.…”

Section: Slika 1 Ulaz U Transformer Je Sekvenca Od Više Tokena Gde unclassified

“…Za implementaciju sistema korišćen je programski jezik Python i biblioteke NumPy 7 , Scikit-learn 8 i PyTorch 9 , koje su služile za pretprocesiranje podataka, računanje Fmere, kreiranje, obučavanje i evaluaciju modela. Korišćena je imbalanced-learn 10 implementacija SMOTE tehnike za balansiranje podataka.…”

Section: Implementacija Sistemaunclassified

Automatska Detekcija Indikatora Loše Dizajniranog Koda Bazirana Na Istoriji Promena Koda

Prokić¹

2020

Zbornik radova FTN

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Kod niskog kvaliteta sadrži strukture (code smells) koje otežavaju održavanje i dalji razvoj softvera. U ovom radu predstavljen je model zasnovan na mašinskom učenju za automatsku detekciju indikatora loše dizajniranog koda (code smell-ova) baziranu na istoriji promena koda. Ulaz modela su vrednosti metrika softverskog koda, izračunate u n revizija za posmatrani isečak koda. Izlaz iz modela je labela koja označava da li posmatrani isečak koda sadrži indikator loše dizajniranog koda ili ne. Studija slučaja izvršena je na detekciji klasa sa mnogo odgovornosti (God Class). Predloženi su koraci za poboljšanje i dalji razvoj arhitekture.

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