2016 IEEE 24th International Conference on Program Comprehension (ICPC) 2016
DOI: 10.1109/icpc.2016.7503721
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Cited by 38 publications
(24 citation statements)
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“…In particular, we check that the proposed approach/methodology 1) meets market constraints (in terms of scalability and usability in practice), 2) is evaluated based on a constructed reference dataset (whatever its size and representativeness), 3) explicitly accounts for app obfuscation (to any extent), and 4) attempts to reduce the noise of common libraries. Details in Table 3 show that no approach addresses all challenges, with Market-scale constraints being the least tackled in the [36] Symptom discovery string offset order CodeMatch [19] Similarity Comparison code fuzzy hash FUIDroid [22] Similarity Comparison layout tree APPraiser [23] Similarity Comparison resource files RepDroid [24] Similarity Comparison layout group graph SimiDroid [25] Similarity Comparison method statements, resource files, components GroupDroid [26] Similarity Comparison control flow graph CLANdroid [27] Similarity Comparison Identifiers, APIs, Intents, Permissions, and Sensors Li et al [31] Similarity Comparison method-level signature RepDetector [33] Similarity Comparison inputs/outputs of methods Wu et al [44] Similarity Comparison HTTP distance FSquaDRA2 [30] Similarity Comparison signature of resource files SUIDroid [34] Similarity Comparison layout tree DroidClone [29] Similarity Comparison control flow pattern Niu et al [32] Similarity Comparison method-level signature AndroSimilar2 [37] Similarity Comparison entropy of byte block AndroSimilar [62] Similarity Comparison entropy of byte block DroidEagle [39] Similarity Comparison visual resources ImageStruct [40] Similarity Comparison images Soh et al [43] Similarity Comparison user interfaces Chen et al [38] Similarity Comparison method-level signature powered by NiCad [89] MassVet [41] Similarity Comparison centroid of UI structures, method-call graphs DroidKin [50] Similarity Comparison meta-info and n-gram bytecode/opcode Ruiz et al [58] Similarity Comparison count-, set-, sequence-, and relationship-based objects Linares-Vásquez et al [55] Similarity Comparison count-, set-, sequence-, and relationship-based objects Chen et al…”
Section: Taxonomy Of Approachesmentioning
confidence: 99%
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“…In particular, we check that the proposed approach/methodology 1) meets market constraints (in terms of scalability and usability in practice), 2) is evaluated based on a constructed reference dataset (whatever its size and representativeness), 3) explicitly accounts for app obfuscation (to any extent), and 4) attempts to reduce the noise of common libraries. Details in Table 3 show that no approach addresses all challenges, with Market-scale constraints being the least tackled in the [36] Symptom discovery string offset order CodeMatch [19] Similarity Comparison code fuzzy hash FUIDroid [22] Similarity Comparison layout tree APPraiser [23] Similarity Comparison resource files RepDroid [24] Similarity Comparison layout group graph SimiDroid [25] Similarity Comparison method statements, resource files, components GroupDroid [26] Similarity Comparison control flow graph CLANdroid [27] Similarity Comparison Identifiers, APIs, Intents, Permissions, and Sensors Li et al [31] Similarity Comparison method-level signature RepDetector [33] Similarity Comparison inputs/outputs of methods Wu et al [44] Similarity Comparison HTTP distance FSquaDRA2 [30] Similarity Comparison signature of resource files SUIDroid [34] Similarity Comparison layout tree DroidClone [29] Similarity Comparison control flow pattern Niu et al [32] Similarity Comparison method-level signature AndroSimilar2 [37] Similarity Comparison entropy of byte block AndroSimilar [62] Similarity Comparison entropy of byte block DroidEagle [39] Similarity Comparison visual resources ImageStruct [40] Similarity Comparison images Soh et al [43] Similarity Comparison user interfaces Chen et al [38] Similarity Comparison method-level signature powered by NiCad [89] MassVet [41] Similarity Comparison centroid of UI structures, method-call graphs DroidKin [50] Similarity Comparison meta-info and n-gram bytecode/opcode Ruiz et al [58] Similarity Comparison count-, set-, sequence-, and relationship-based objects Linares-Vásquez et al [55] Similarity Comparison count-, set-, sequence-, and relationship-based objects Chen et al…”
Section: Taxonomy Of Approachesmentioning
confidence: 99%
“…Finally, we investigate how the accuracy of repackaged [19] (10000,100000) DR-Droid2 [20] (1000,10000) DAPASA [21] (10000,100000) FUIDroid [22] (10000,100000) APPraiser [23] (1000000, ∞) RepDroid [24] (100,1000) SimiDroid [25] (1000,10000) GroupDroid [26] (1000,10000) CLANdroid [27] (10000,100000) DR-Droid [28] (1000,10000) DroidClone [29] (100,1000) FSquaDRA2 [30] (1000,10000) Li et al [31] α (1000000, ∞) Niu et al [32] -RepDetector [33] (1000,10000) SUIDroid [34] (100000,1000000) Kim et al [35] (100,1000) AndroidSOO [36] (10000,100000) AndroSimilar2 [37] (10000,100000) Chen et al [38] (1000,10000) DroidEagle [39] (1000000, ∞) ImageStruct [40] (10000,100000) MassVet [41] (1000000, ∞) PICARD [42] (0,100) Soh et al [43] (100,1000) Wu et al [44] (1000,10000) WuKong [45] (100000,1000000) AnDarwin2 [46] (100000,1000000) AndRadar [47] (100000,1000000) Chen et al [48] (10000,100000) DIVILAR [49] (0,100) DroidKin [50] (1000,10000) DroidLegacy [51] (1000,10000) DroidMarking [9] (100,1000) DroidSim [52] (100,1000) FSquaDRA [53] (10000,100000) Kywe et al [54] (10000,100000) Linares-Vásquez et al [55] α (10000,100000) PlayDrone [56] α (1000000, ∞) ResDroid [57] (1000...…”
Section: Review Of Evaluation Setups and Artefactsmentioning
confidence: 99%
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“…The second one is based on semantic information. CLANdroid [30] detects similar apps through analyzing five semantic anchors (e.g., identifiers and Android APIs). The third kind leverages lib detection methods.…”
Section: Related Workmentioning
confidence: 99%
“…Wilde et al [3] proposed the earliest CIA method, known as software reconnaissance. After more than 20 years of development, it has become the foundational technology in many fields, including components retrieval [4], software reuse [5], and requirements traceability analysis [6].…”
Section: Introductionmentioning
confidence: 99%