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AndroEvolve: automated Android API update with data flow analysis and variable denormalization

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Abstract

The Android operating system is frequently updated, with each version bringing a new set of APIs. New versions may involve API deprecation; Android apps using deprecated APIs need to be updated to ensure the apps’ compatibility with old and new Android versions. Updating deprecated APIs is a time-consuming endeavor. Hence, automating the updates of Android APIs can be beneficial for developers. CocciEvolve is the state-of-the-art approach for this automation. However, it has several limitations, including its inability to resolve out-of-method variables and the low code readability of its updates due to the addition of temporary variables. In an attempt to further improve the performance of automated Android API update, we propose an approach named AndroEvolve, that addresses the limitations of CocciEvolve through the addition of data flow analysis and variable name denormalization. Data flow analysis enables AndroEvolve to resolve the value of any variable within the file scope. Variable name denormalization replaces temporary variables that may present in the CocciEvolve update with appropriate values in the target file. We have evaluated the performance of AndroEvolve and the readability of its updates on 372 target files containing 565 deprecated API usages. Each target file represents a file from an Android application that uses a deprecated API in its code. AndroEvolve successfully updates 481 out of 565 deprecated API invocations correctly, achieving an accuracy of 85.1%. Compared to CocciEvolve, AndroEvolve produces 32.9% more instances of correct updates. Moreover, our manual and automated evaluation shows that AndroEvolve updates are more readable than CocciEvolve updates.

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Notes

  1. We use the Sample Size Calculator from the Australia Bureau of Statistics that supports stratified random sampling. The calculator is available from: https://www.abs.gov.au/websitedbs/D3310114.nsf/Home/Sample+Size+Calculator+Stratification+Examples

  2. Landis and Koch (1977) define a kappa score in the range of 0.00-0.20 as slight agreement, 0.21-0.40 as fair agreement, 0.41-0.60 as moderate agreement, 0.61 and 0.80 as substantial agreement, and 0.81-1.00 as almost perfect agreement, respectively.

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Funding

This research is supported by the Singapore National Research Foundation (award number: NRF2016-NRF-ANR003) and the ANR ITrans project.

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Authors and Affiliations

  1. School of Computing and Information Systems, Singapore Management University, Singapore, Singapore

    Stefanus A. Haryono, Ferdian Thung, David Lo, Lingxiao Jiang & Hong Jin Kang

  2. Inria, Paris, France

    Julia Lawall & Gilles Muller

  3. Sorbonne University/Inria/LIP6, Paris, France

    Lucas Serrano

Authors
  1. Stefanus A. Haryono
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  2. Ferdian Thung
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  3. David Lo
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  4. Lingxiao Jiang
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  5. Julia Lawall
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  6. Hong Jin Kang
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  7. Lucas Serrano
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  8. Gilles Muller
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Correspondence to Stefanus A. Haryono.

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Communicated by: Gabriele Bavota

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Haryono, S.A., Thung, F., Lo, D. et al. AndroEvolve: automated Android API update with data flow analysis and variable denormalization. Empir Software Eng 27, 73 (2022). https://doi.org/10.1007/s10664-021-10096-0

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