The Java system dependence graph

The Program Dependence Graph was introduced by Ottenstein and Ottenstein in 1984 [14]. It was suggested to be a suitable internal program representation for monolithic programs, for the purpose of carrying out certain software engineering operations such as slicing and the computation of program metrics. Since then, Horwitz et al. have introduced the multi-procedural equivalent System Dependence Graph [9]. Many authors have proposed object-oriented dependence graph construction approaches [11, 10, 20, 12]. Every approach provides its own benefits, some of which are language specific. This paper is based on Java and combines the most important benefits from a range of approaches. The result is a Java System Dependence Graph, which summarises the key benefits offered by different approaches and adapts them (if necessary) to the Java language

Development of a Tool for Slicing of Object-Oriented Program

Program slicing has many applications in a software development environment such as debugging, testing, anomaly detection, program understanding and many more. The concept being introduced by Weiser and it was started with static slicing calculation. Talking about static slicing, it is a subset of statements of a program which directly or indirectly affect the values of the variables computed providing a slicing criterion. Dynamic slicing is the counterpart of the static slicing i.e finding the statements which are really affected by giving the particular input value of the variable. Object-Oriented Program(OOP) has been the most widely used software development technique. OOP is still popular among many companies for their product development.There are some drawbacks of the OOP implementation. One of them is cross-cutting concerns. Aspect-Oriented Program provides separation of cross-cutting concerns from the core modules by introducing a new unit of modularization, called Aspect. In this project, we have developed a Tool which creates System dependence Graph(SDG) which is the intermediate representation of an OOP and AOP , then takes that SDG as input to compute the slicing of that program with respect to slicing criterion

Analysis of Java Programs Using Bytecode Based Frameworks

Java SDG(System dependence Graph) API and JOANA (Java Object-sensitive Analysis) are two bytecode based analysis frameworks available for analyzing object oriented java programs for different applications. In the present era, the continuous evolution of the customer expectations and requirements has resulted in the increase of size of the software. This arises the problems in maintaining software. Both the frameworks i.e Java SDG API and Joana consist of different variety of analysis techniques which are based on dependence graph generation and computation of slices of an input program. In our work, we make a comparative analysis study on the effectiveness and efficiency of both these above mentioned analysis frameworks in generating the corresponding intermediate dependence graph and computing slices. The dependence graph we have generated is SDG and we have used backward slicing approach in order to compute slices. The two-phase graph reachability algorithm is used in our work in case of Java SDG API in order to perform slicing. The two web start applications are used in order to generate and view SDG in case of Joana which are IFC console and Joana graph viewer. The analysis is based on the bytecode of the program under consideration. The experimental analysis shows that Joana can be extended for more diverse applications

System dependence graphs in sequential Erlang

The system dependence graph (SDG) is a data structure used in the imperative paradigm for different static analysis, and particularly, for program slicing. Program slicing allows us to determine the part of a program (called slice) that influences a given variable of interest. Thanks to the SDG, we can produce precise slices for interprocedural programs. Unfortunately, the SDG cannot be used in the functional paradigm due to important features that are not considered in this formalism (e.g., pattern matching, higher-order, composite expressions, etc.). In this work we propose the first adaptation of the SDG to a functional language facing these problems. We take Erlang as the host language and we adapt the algorithms used to slice the SDG to produce precise slices of Erlang interprocedural programs. As a proof-of-concept, we have implemented a program slicer for Erlang based on our SDGs.This work has been partially supported by the Spanish Ministerio de Ciencia e Innovaci´on under grant TIN2008-06622-C03-02 and by the Generalitat Valenciana under grant PROMETEO/2011/052. Salvador Tamarit was partially supported by the Spanish MICINN under FPI grant BES-2009-015019Silva Galiana, JF.; Tamarit Muñoz, S.; Tomás Franco, C. (2012). System dependence graphs in sequential Erlang. En Fundamental Approaches to Software Engineering. Springer Verlag (Germany). 486-500. https://doi.org/10.1007/978-3-642-28872-2_33S486500Agrawal, H., Horgan, J.R.: Dynamic program slicing. In: Programming Language Design and Implementation (PLDI), pp. 246–256 (1990)Brown, C.: Tool Support for Refactoring Haskell Programs. PhD thesis, School of Computing, University of Kent, Canterbury, Kent, UK (2008)Cheda, D., Silva, J., Vidal, G.: Static slicing of rewrite systems. Electron. Notes Theor. Comput. Sci. 177, 123–136 (2007)Ferrante, J., Ottenstein, K.J., Warren, J.D.: The Program Dependence Graph and Its Use in Optimization. ACM Transactions on Programming Languages and Systems 9(3), 319–349 (1987)Field, J., Ramalingam, G., Tip, F.: Parametric program slicing. In: Proceedings of the 22nd ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL 1995, pp. 379–392. ACM, New York (1995)Horwitz, S., Reps, T., Binkley, D.: Interprocedural slicing using dependence graphs. ACM Transactions Programming Languages and Systems 12(1), 26–60 (1990)Korel, B., Laski, J.: Dynamic Program Slicing. Information Processing Letters 29(3), 155–163 (1988)Larsen, L., Harrold, M.J.: Slicing object-oriented software. In: Proceedings of the 18th International Conference on Software Engineering, ICSE 1996, pp. 495–505. IEEE Computer Society, Washington, DC (1996)Liang, D., Harrold, M.J.: Slicing objects using system dependence graphs. In: Proceedings of the International Conference on Software Maintenance, ICSM 1998, pp. 358–367. IEEE Computer Society, Washington, DC (1998)Lindahl, T., Sagonas, K.F.: Typer: a type annotator of erlang code. In: Sagonas, K.F., Armstrong, J. (eds.) Erlang Workshop, pp. 17–25. ACM (2005)Lindahl, T., Sagonas, K.F.: Practical type inference based on success typings. In: Bossi, A., Maher, M.J. (eds.) PPDP, pp. 167–178. ACM (2006)Ochoa, C., Silva, J., Vidal, G.: Dynamic slicing based on redex trails. In: Proceedings of the 2004 ACM SIGPLAN Symposium on Partial Evaluation and Semantics-Based Program Manipulation, PEPM 2004, pp. 123–134. ACM, New York (2004)Reps, T., Turnidge, T.: Program Specialization via Program Slicing. In: Danvy, O., Thiemann, P., Glück, R. (eds.) Dagstuhl Seminar 1996. LNCS, vol. 1110, pp. 409–429. Springer, Heidelberg (1996)Rodrigues, N.F., Barbosa, L.S.: Component identification through program slicing. In: Proc. of Formal Aspects of Component Software (FACS 2005). Elsevier ENTCS, pp. 291–304. Elsevier (2005)Tip, F.: A survey of program slicing techniques. Journal of Programming Languages 3(3), 121–189 (1995)Tóth, M., Bozó, I., Horváth, Z., Lövei, L., Tejfel, M., Kozsik, T.: Impact Analysis of Erlang Programs Using Behaviour Dependency Graphs. In: Horváth, Z., Plasmeijer, R., Zsók, V. (eds.) CEFP 2009. LNCS, vol. 6299, pp. 372–390. Springer, Heidelberg (2010)Walkinshaw, N., Roper, M., Wood, M., Roper, N.W.M.: The java system dependence graph. In: Third IEEE International Workshop on Source Code Analysis and Manipulation, p. 5 (2003)Weiser, M.: Program Slicing. In: Proceedings of the 5th International Conference on Software Engineering, pp. 439–449. IEEE Press (1981)Widera, M.: Flow graphs for testing sequential erlang programs. In: Proceedings of the 2004 ACM SIGPLAN Workshop on Erlang, ERLANG 2004, pp. 48–53. ACM, New York (2004)Widera, M., Informatik, F.: Concurrent erlang flow graphs. In: Proceedings of the Erlang/OTP User Conference (2005)Zhao, J.: Slicing aspect-oriented software. In: Proceedings of the 10th International Workshop on Program Comprehension, IWPC 2002, pp. 251–260. IEEE Computer Society, Washington, DC (2002

Extracting and verifying coordination models from source code

Current software development relies increasingly on non-trivial coordination logic for combining autonomous services often running on different platforms. As a rule, however, intypical non-trivial software systems, such a coordination layer is strongly weaved within the application at source code level. Therefore, its precise identification becomes a major methodological (and technical) problem which cannot be overestimated along any program understanding or refactoring process. Open access to source code, as granted in OSS certification, provides an opportunity for the development of methods and technologies to extract, from source code, the relevant coordination information. This paper is a step in this direction, combining a number of program analysis techniques to automatically recover coordination information from legacy code. Such information is then expressed as a model in Orc, a general purpose orchestration language

Field-Sensitive Program Slicing

The granularity level of the program dependence graph (PDG) for composite data structures (tuples, lists, records, objects, etc.) is inaccurate when slicing their inner elements. We present the constrained-edges PDG (CE-PDG) that addresses this accuracy problem. The CE-PDG enhances the representation of composite data structures by decomposing statements into a subgraph that represents the inner elements of the structure, and the inclusion and propagation of data constraints along the CE-PDG edges allows for accurate slicing of complex data structures. Both extensions are conservative with respect to the PDG, in the sense that all slicing criteria (and more) that can be specified in the PDG can be also specified in the CE-PDG, and the slices produced with the CE-PDG are always smaller or equal to the slices produced by the PDG. An evaluation of our approach shows a reduction of the slices of 11.67%/5.49% for programs without/with loops

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

Comparing text-based and dependence-based approaches for determining the origins of bugs

Identifying bug origins – the point where erroneous code was introduced – is crucial for many software engineering activities, from identifying process weaknesses to gathering data to support bug detection tools. Unfortunately, this information is not usually recorded when fixing bugs, and recovering it later is challenging. Recently, the text approach and the dependence approach have been developed to tackle this problem. Respectively, they examine textual and dependence-related changes that occurred prior to a bug fix. However, only limited evaluation has been carried out, partially because of a lack of available implementations and of datasets linking bugs to origins. To address this, origins of 174 bugs in three projects were manually identified and compared to a simulation of the approaches. Both approaches were partially successful across a variety of bugs – achieving 29–79% precision and 40–70% recall. Results suggested the precise definition of program dependence could affect performance, as could whether the approaches identified a single or multiple origins. Some potential improvements are explored in detail and identify pragmatic strategies for combining techniques along with simple modifications. Even after adopting these improvements, there remain many challenges: large commits, unrelated changes and long periods between origins and fixes all reduce effectiveness