Benevol 2011

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Alexander used these slides during for his presentation at BeNeVol 2011 in Brussels, Belgium. That is after he blew the fuses in the entire building.

Paper:
Serebrenik A, Vasilescu B and van den Brand M (2011), "Similar tasks, different effort: Why the same amount of functionality requires different development effort?", In Proceedings of the 10th Belgian-Netherlands Software Evolution Seminar, pp. 4-5.

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  • B. Kitchenham, S. L. Pfleeger, B. McColl, and S. Eagan, “An empirical study of maintenance and development estimation accuracy,” Journal of Systems and Software, vol. 64, no. 1, pp. 57–77, 2002.
  • > lm2 <- lm(log(SWE)~log(AFP))> summary(lm2)Call:lm(formula = log(SWE) ~ log(AFP))Residuals: Min 1Q Median 3Q Max -4.3960 -0.6584 0.0272 0.6760 3.3857 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.92717 0.09386 31.19 <2e-16 ***log(AFP) 0.84617 0.01891 44.75 <2e-16 ***---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.024 on 1607 degrees of freedomMultiple R-squared: 0.5548, Adjusted R-squared: 0.5545 F-statistic: 2003 on 1 and 1607 DF, p-value: < 2.2e-16 df6$residuals <- lm2$residualsdiffEff_ineqMeasures_on_df(df6)
  • Benevol 2011

    1. 1. Similar Tasks, Different Effort:Why the Same Amount ofFunctionality RequiresDifferent Development Effort? Alexander Serebrenik Bogdan Vasilescu Mark van den Brand
    2. 2. Why do some systems require more effort? • Empirical study • ISBSG version 11 • largest publically available collection: 5052 projects • 118 project attributes, including − amount of functionality − work effort • Not all projects are suited for the study • self-reporting different data quality • different ways of measuring project attributes/ W&I / MDSE 23-4-2012 PAGE 1
    3. 3. Project selection ISBSG v.11 5052 Effort Staff hours (recorded) 3537 Full development lifecycle 2261 Project-specific activities only 2079 Functionality IFPUG 1661 Data quality “A” or “B” 1609/ W&I / MDSE 23-4-2012 PAGE 2
    4. 4. Effort and Functionality Distributions • Effort: • Adjusted FP or unadjusted FP • skewed, outliers • Adjusted is more reliable [Kitchenham et al. JSS, 2002] • skewed, outliers/W&I / MDSE 23-4-2012 PAGE 3
    5. 5. More functionality more effort required • Log-transformation for the skewness / outliers problem • Adequate • p-value for the F- stat ≤ 2.2*10-16, • p-values intercept and coefficient ≤ log(SWE) = 2.2*10-16, 2.92717 + • residuals show a 0.84617 * log(AFP) chaotic pattern/ W&I / MDSE 23-4-2012 PAGE 4
    6. 6. Why do some systems require more effort? • Closer look at the residuals • technical aspects: − primary programming language, language type, development type, platform, and architecture • organization type • intended market • year of project • Problem of ISBSG • missing values due to self-reporting/ W&I / MDSE 23-4-2012 PAGE 5
    7. 7. What attributes impact the development effort? • Goal: compare different project attributes • ISBSG – 118 attributes • Remove projects with missing values • More attributes less projects • Keep projects with missing values • NA-category becomes too important • We choose • primary programming language, language type, organization type, intended market, year of project, development type, platform, architecture/ W&I / MDSE 23-4-2012 PAGE 6
    8. 8. Explanation of impact • Partition individuals in groups • Partition = explanation [Cowell, Jenkins 1995] • Inequality within the groups and between the groups − Inequality indices • Better explanation: more inequality between the groups − Lila is better than red − Partition refinement doesn’t deteriorate the explanation/ SET / W&I / TU/e PAGE 7
    9. 9. Which inequality index? • We need a decomposable index applicable to negative values/ W&I / MDSE 23-4-2012 PAGE 8
    10. 10. Results Indonesia:Project attribute Explanation % expenditure by educ.level 32.6% missing values No Missing values N = 151 N = 1609Primary Indonesia: 25,37% 16,11%programming expenditure by Linux: LOC bylanguage province 18.9% package 17.4%Organisation type 17,59% 18,36%Year of the project 10,88% 5,41%Architecture 8,68% Linux: LOC by 3,35%Development 5,43% impl lang 5.32% 5,05%PlatformIndonesia:Intended Market by expenditure 4,61% 1,57% Linux: LOC byLanguage type2.6% gender 2,45% maintainer 4.45% 1,28%Development Type/ W&I / MDSE 23-4-2012 PAGE 9 0,05% 0,07%
    11. 11. Conclusions • Three groups of attributes • High-impact: primary programming language, organization type • Middle-impact − year of the project [cf. Kitchenham et al. 2002] − architecture, development platform • Low impact: intended market, language type, devel’t type • A new technique for analysis of effort fp/ W&I / MDSE 23-4-2012 PAGE 10
    12. 12. Future work • Partition should be MECE • “Wholesale & Retail Trade” and “Financial, Property & Business Services” • New aggregation/explanation techniques • Conjecture: relative importance of attributes will be the same for other datasets • Models based on data from multiple companies are not applicable when one company data is considered [Ruhe 1999] • Both multi-company and company-specific studies are needed/ W&I / MDSE 23-4-2012 PAGE 11

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