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A Validation of Object-Oriented Design Metrics as Quality Indicators
- 1. A Validation of Object-Oriented Design Metrics as Quality Indicators Evi Yulianti 1006833110 Iis Solichah 1006800094 Mubarik Ahmad 1006833294 Victor R. Basili Lionel C. Briand Walcelio L. Melo University of Mariland
- 4. Author Dr. Victor R. Basili (University of Maryland) Department of Computer Science , Professor, 1970 – Present Institute for Advanced Computer Studies , Research Professor, 1984-Present Dr. Lionel C. Briand (Carleton University) Canada Research Chair (Tier I) in Software Quality Engineering Dr. Walcelio (Walt) L. Melo, Professor, Catholic University of Brasilia, DF, Brazil , 1997-2001 Lead Architect, Model Driven Solutions, 2008-now
- 5. Introduction
- 6. Introduction Time & resource consuming activity help manager : 1. make decisions, plan and schedule activities, 2. allocate resources for the different software activities identify fault-prone modules TESTING SOFTWARE METRIC … ?
- 7. Introduction (cont’) Metrics must be defined and validated in order to be used in industry Empirical validation aims at demonstrating the usefulness of a measure in practice and is, therefore, a crucial activity to establish the overall validity of a measure. ability to identify fault-prone classes
- 10. Data Analysis
- 24. CASE STUDY: DATA ANALYSIS FOR SAMPLE CLASSES
- 25. Class Diagram
- 30. SPSS V.15 VARIABLE VIEW
- 31. DATA VIEW
- 33. coefficient constant z = - 0,513 + 0,075*WMC R 2 Odds ratio π = exp(z) / (1+exp(z))
- 34. WMC z= -0,513 + 0,075*WMC π = exp(z) / (1+exp(z)) Class WMC π Tool.java 1 0,392218 CTextbox.java 1 0,392218 DrawingPackage.java 3 0,428494 Screen.java 5 0,465555 CCircle.java 8 0,521736 ShapeList.java 10 0,558974 CRect.java 12 0,59556 CShape.java 15 0,648397
- 35. DIT z= -1,386 + 21,566*DIT π = exp(z) / (1+exp(z)) Class DIT π Tool.java 0 0,200047 Screen.java 0 0,200047 ShapeList.java 0 0,200047 CShape.java 0 0,200047 DrawingPackage.java 0 0,200047 CCircle.java 1 1 CRect.java 1 1 CTextbox.java 2 1
- 36. NOC z= 0,196 - 0,5309*NOC π = exp(z) / (1+exp(z)) Class NOC π Tool.java 0 0,548844 Screen.java 0 0,548844 ShapeList.java 0 0,548844 CCircle.java 0 0,548844 CTextbox.java 0 0,548844 DrawingPackage.java 0 0,548844 CRect.java 1 0,417049 CShape.java 2 0,296129
- 37. CBO z= -2,884 – 2,027*CBO π = exp(z) / (1+exp(z)) Class CBO π Screen.java 0 0,05295 Tool.java 1 0,297967 ShapeList.java 1 0,297967 CShape.java 1 0,297967 CCircle.java 2 0,763145 CRect.java 2 0,763145 CTextbox.java 2 0,763145 DrawingPackage.java 2 0,763145
- 38. RFC z= -0,941 + 0,09*RFC π = exp(z) / (1+exp(z)) Class RFC π Tool.java 1 0,299223 Screen.java 5 0,379658 CTextbox.java 6 0,401072 CCircle.java 9 0,467297 CRect.java 14 0,579081 CShape.java 15 0,600848 DrawingPackage.java 15 0,600848 ShapeList.java 18 0,663515
- 39. LCOM z= 0,288 - 0,231*LCOM π = exp(z) / (1+exp(z)) Class LCOM π Tool.java 0 0,571506429 Screen.java 0 0,571506429 ShapeList.java 0 0,571506429 CCircle.java 0 0,571506429 CRect.java 0 0,571506429 CTextbox.java 0 0,571506429 DrawingPackage.java 0 0,571506429 CShape.java 93 6,23919E-10
- 42. Related Works
- 43. CONCLUSION & FUTURE WORK
Hinweis der Redaktion
- Tabel 1 menunjukkan bahwa class-class yang diobservasi rata-rata memiliki: -DIT (kedalaman inheritance) rendah NOC rendah (rata-rata class hanya memiliki sedikit children) LCOM juga rendah (rata-rata class memiliki high-cohesion) Dari data tersebut, dapat dilihat bahwa Metrics tersebut tidak dapat melakukan differentiate terhadap sample classes.
- Korelasi antar metric rendah. Hanya yang dicetak tebal yang memiliki korelasi cukup signifikan. Pada scatterplots: relationship antara CBO dan RFC tidak disebabkan oleh outliers.
- Logistic regression: a standard technique based on maximum likelihood estimation, to analyze the relationships between metrics and the faultproneness of classes. Univariate -> to evaluate the relationship of each of the metrics in isolation and faultproneness Multivariate -> to evaluate the predictive capability of those metrics that had been assessed sufficiently significant in the univariate analysis Formula di atas adalah persamaan relasi untuk multivariate logistic regression. Univariate adalah salah satu kasus khusus dari multivariate (ketika hanya satu variable yang muncul dalam persamaan). Phi menyatakan probabilitas ditemukannya fault pada class saat validasi. Xi menyatakan design metrics sebagai explanatory variable pada model. ( covaviates of the logistic regression equation )
- Data statistik yang digunakan pada tabel 3 dan 4: coefficient: semakin besar koefisien, maka pengaruh explanatory variable terhadap respons variable semakin besar. : selisih odd-ratio -> menunjukkan penambahan/pengurangan odd ratio ketika X bertambah satu unit. (X): menunjukkan pengaruh metrics terhadap variable yang diprediksi. p-value: keakuratan estimasi koefisien (selisih koefisien)
- Penjelasan NOC: Kebanyakan class dalam sample tidak memiliki lebih dari satu anak. Reuse merupakan faktor signifikan negatif terhadap fault-density [5]. Large NOC are less fault-prone. Penjelasan LCOM: LCOM tidak dapat dianalisis untuk memprediksi nilai probabilitas fault, karena nilai-nilai yang seharusnya negatif dalam LCOM direset menjadi nol. Hal ini mengakibatkan tidak dapat dilakukannya perbandingan yang fair antar class.
- Dari total 58 class yang pada actual case memiliki fault, kita bisa memprediksikan 48 class faulty. Dari total 268 fault yang pada actual case terjadi, kita bisa memprediksikan 250 fault. To summarize, results show that the studied OO metrics are useful predictors of fault-proneness.
- Code metric ini disediakan oleh Amadeus tool [2]. Mendeteksi 112 class sebagai faulty (actual faulty: 58 class). Terdapat 61 class yang harus diperiksa, padahal bukan faulty.
- in logistic regression R2 > 0,3 is considered good so our model seems to be a good predictor.