Program slicing of Java programs

[EN] Program slicing is a technique to extract the part of the program that can affect the values computed at a given program point (known as the slicing criterion). To represent programs, program slicing uses the System Dependence Graph (SDG), for which several extensions like the Java System Dependence Graph (JSysDG) or the Sub-Statement Linear Dependence Graph (SSLDG) exist to deal with Java object-oriented programs. In this paper, we present an incompleteness result proving that these graphs do not produce complete slices in all cases, and specifically when some object variables are selected as the slicing criterion. We first identify the source of the problem: the representation of dependences between partial definitions of objects is ill-defined in these approaches, leading to a loss of completeness in many cases. To solve this limitation, we extend these representations with the addition of a specific flow dependence for object type variables called object-flow dependence. This extension provides a more accurate flow representation between object variables and its data members and it allows us to obtain complete slices when an object variable is selected as the slicing criterion.(c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).This work has been partially supported by grant PID2019-104735RB-C41 funded by Spanish MCI/AEI/10.13039/501100011033, by the Generalitat Valenciana under grant Prometeo/2019/098 (DeepTrust), and by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215. Carlos Galindo was partially supported by the Spanish Ministerio de Universidades under grant FPU20/03861. Sergio Perez was partially supported by Universitat Politecnica de Valencia under FPI grant PAID-01-18.Galindo, C.; Pérez, S.; Silva, J. (2023). Program slicing of Java programs. Journal of Logical and Algebraic Methods in Programming. 130. https://doi.org/10.1016/j.jlamp.2022.10082613