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axa
fb0791cb9a0347c2e765547994339718026160cc8e50e1a37ff2437c1cb60ba2
[ "Table 1: Extensions and Changes of Single-Language Analyses for Integration into AXA", "Table 2: Benchmark Results", "Table 3: Precision And Recall of Points-To-Sets" ]
[ { "Analysis": { "0": "Java", "1": "JavaScript", "2": "Native" }, "Detector": { "0": "836 (JS), 0 (Native)", "1": "166 + 2", "2": 328 }, "Lattice": { "0": "60 (JS)", "1": "90+14", "2": 107 }, "Solver": { "0": 0, "1": 2, ...
[ { "Analysis": { "0": "Java", "1": "JavaScript", "2": "Native" }, "Detector": { "0": "836 (JS), 0 (Native)", "1": "166 + 2", "2": "<obf>" }, "Lattice": { "0": "60 (JS)", "1": "90+14", "2": "<obf>" }, "Solver": { "0": "<obf>", ...
[]
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bcia
815d31820a9a4c03bcae7d29320f7cff8565ca12465f4b98fd0fd70fd5959452
[ "Table 2: Overview of selected subject projects." ]
[ { "Project": { "0": "P1: Spectre", "1": "P2: Paddle", "2": "P3: AliceO2", "3": "P4: Krita", "4": "P5: MySQL-Server", "5": "P6: Qt-Creator", "6": "P7: Serenity", "7": "P8: Calligra", "8": "P9: VXL", "9": "P10: Swift" }, "# BC a": { "0": 30...
[ { "Project": { "0": "P1: Spectre", "1": "P2: Paddle", "2": "P3: AliceO2", "3": "P4: Krita", "4": "P5: MySQL-Server", "5": "P6: Qt-Creator", "6": "P7: Serenity", "7": "P8: Calligra", "8": "P9: VXL", "9": "P10: Swift" }, "# BC a": { "0": "<...
[ { "table_index": 0, "caption": "Table 1: The types of relationships in the IKG.", "locations": [ { "page_no": 5, "bbox": { "l": 318.0298156738281, "t": 686.34619140625, "r": 558.110595703125, "b": 423.79833984375, "coord_origin": "B...
[ { "figure_index": 0, "caption": "", "reason": "missing_caption" }, { "figure_index": 1, "caption": "", "reason": "missing_caption" }, { "figure_index": 2, "caption": "Figure 1: Overview of BCIA.", "reason": "no_data" }, { "figure_index": 3, "caption": "", ...
true
null
[ "(a) General perspective.", "(b) Project-specific perspective.", "Figure 5: The distribution of the magnitude of impact sets.", "Figure 6: The distribution of the breadth of impact sets." ]
bloat
293791420dc4a1f6b3e3372f93b42337f6978e24ea5df1fac450e830f4373af9
[ "Table 1. The evolution of our initial dataset [Alfadel M 2020] after applying each step of our data collection and data analysis approach.", "Table 2. Statistics on the resolved and unresolved external calls during our stitching process.", "Table 3. The status of our pull requests, proposing the removal of blo...
[ { "Step": { "0": "", "1": "Data Collection", "2": "", "3": "Data Analysis", "4": "", "5": "" }, "Operation": { "0": "Initial dataset of Python GitHub projects", "1": "Filtering inaccessible projects", "2": "Dependency resolution", "3": "Partial...
[ { "Step": { "0": "", "1": "Data Collection", "2": "", "3": "Data Analysis", "4": "", "5": "" }, "Operation": { "0": "Initial dataset of Python GitHub projects", "1": "Filtering inaccessible projects", "2": "Dependency resolution", "3": "Partial...
[]
[ { "figure_index": 0, "caption": "", "reason": "missing_caption" }, { "figure_index": 1, "caption": "", "reason": "missing_caption" }, { "figure_index": 2, "caption": "Fig. 1. The overview of our approach for studying bloated code in PyPI ecosystem.", "reason": "no_data" ...
true
null
[ "Fig. 5. The distribution of bloat metrics per granularity. Each entry indicates the percentage of bloated entities (e.g., files, methods), and the size of bloated dependency code compared to the overall LoC.", "Fig. 6. The distribution of bloat metrics per vulnerability exposure. Each entry indicates the relatio...
crossover
876e1b386f47bf84128d3548de087430e06d589826e68f61333585c3961b5945
[ "Table 1: Evaluation Subjects. For each subject, we list the project name and version (Project), the format of the input (Format), and the number of branches as reported by JaCoCo (Branches).", "Table 2: Heritability Metrics. For each crossover operator, we report the proportion of samples that were hybrids ( HY ...
[ { "Project": { "0": "Apache Ant (1.10.13) [1]", "1": "Apache BCEL (6.7.0) [4]", "2": "Google Closure (v20230502) [13]", "3": "Apache Maven (3.9.2) [5]", "4": "OpenJDK Nashorn (11.0.19) [39]", "5": "Mozilla Rhino (1.7.14) [33]", "6": "Apache Tomcat (10.1.9) [6]" }, ...
[ { "Project": { "0": "Apache Ant (1.10.13) [1]", "1": "Apache BCEL (6.7.0) [4]", "2": "Google Closure (v20230502) [13]", "3": "Apache Maven (3.9.2) [5]", "4": "OpenJDK Nashorn (11.0.19) [39]", "5": "Mozilla Rhino (1.7.14) [33]", "6": "Apache Tomcat (10.1.9) [6]" }, ...
[]
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true
null
[ "Figure 3: Branch Coverage Over Time. Each x-axis is time in minutes and each y-axis is the number of covered branches. Each plot depicts the median number of covered branches (solid line) and the range of covered branches (filled area) across the 20 trials over the duration of the fuzzing campaign for each of the ...
dypybench
738c509d543d896518195331db91b6b059efbb3b24c6f7e1ee2e2250e47b7882
[ "Table 2. Properties of DyPyBench.", "Table 3. Examples of patterns among top-100 mined patterns." ]
[ { "Metric": { "0": "Projects", "1": "Lines of code", "2": "Test cases:", "3": "Total", "4": "Passing", "5": "Failing", "6": "Skipped", "7": "Lines of executed code:", "8": "Total lines", "9": "Coverage", "10": "Execution time:", "11": "Avg....
[ { "Metric": { "0": "Projects", "1": "Lines of code", "2": "Test cases:", "3": "Total", "4": "Passing", "5": "Failing", "6": "Skipped", "7": "Lines of executed code:", "8": "Total lines", "9": "Coverage", "10": "Execution time:", "11": "Avg....
[ { "table_index": 0, "caption": "Table 1. Projects in the benchmark.", "locations": [ { "page_no": 5, "bbox": { "l": 44.755470275878906, "t": 613.0430297851562, "r": 441.7969665527344, "b": 404.2380065917969, "coord_origin": "BOTTOML...
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true
null
[ "Fig. 1. Number of successful, failed, and skipped test cases for each project of the benchmark.", "Fig. 2. Cumulative distribution of test suite pass rates for all projects.", "Fig. 3. Distribution of covered statements vs uncovered statements during the execution of test suites for each project.", "Fig. 4. ...
goblinupdater
b4a038cdf21e429d5d3e23560707fd3dfab0ed7810af222ca77247fa05fab683
[ "Table 1: Configurations used for experiments. F = freshness, P = popularity, CVE = vulnerability score." ]
[ { "id.": { "0": "cfg1", "1": "cfg2", "2": "cfg3", "3": "cfg4", "4": "cfg5", "5": "cfg6" }, "weights.F": { "0": 1, "1": 1, "2": 1, "3": 1, "4": 0.4, "5": 0.4 }, "weights.P": { "0": "-", "1": "-", "2": "-", ...
[ { "id.": { "0": "cfg1", "1": "cfg2", "2": "cfg3", "3": "cfg4", "4": "cfg5", "5": "cfg6" }, "weights.F": { "0": "<obf>", "1": "<obf>", "2": "<obf>", "3": "<obf>", "4": "<obf>", "5": "<obf>" }, "weights.P": { "0": "-", ...
[ { "table_index": 1, "caption": "Table 2: Demographics of our dataset of 107 Java projects", "locations": [ { "page_no": 7, "bbox": { "l": 320.7544250488281, "t": 540.575439453125, "r": 555.2611694335938, "b": 473.236083984375, "coor...
[ { "figure_index": 0, "caption": "", "reason": "missing_caption" }, { "figure_index": 1, "caption": "", "reason": "missing_caption" }, { "figure_index": 2, "caption": "Figure 1: An extended dependency graph rooted in p . This dependency graph for p (purple/dark squares and cir...
true
null
[ "Figure 2: Correctness of the solutions generated by different approaches and configurations", "Figure 3: Freshness of the solutions generated by different approaches and configurations", "Figure 4: Cumulative cost of change of the solutions generated by different approaches and configurations", "Figure 5: Qu...
lasapp
1c47925c6c618b9b779992850641edce3ba48d0af3db6a21a6d12de097db5bf2
[]
[]
[]
[ { "table_index": 0, "caption": "Table 1: Lines of code needed to add LASAPP support for various PPLs and probability distribution back-ends.", "locations": [ { "page_no": 3, "bbox": { "l": 331.6434020996094, "t": 262.339599609375, "r": 544.168029785156...
[ { "figure_index": 0, "caption": "", "reason": "missing_caption" }, { "figure_index": 1, "caption": "", "reason": "missing_caption" }, { "figure_index": 2, "caption": "", "reason": "missing_caption" }, { "figure_index": 3, "caption": "Figure 1: Overview of the ...
true
null
[ "Figure 2: Linear model implemented in Pyro on the top. Results from posterior inference with decreasing uncertainty on the bottom." ]
mutation
aa18fea3dbb4851800c40a7fcc3023ad6fb154d816a4df54bb8cd3a927f2bd27
[ "Table 1: Experimental Subject Programs", "Table 2: Method Exit Anomalies" ]
[ { "Subject Project": { "0": "commons-cli", "1": "joda-money", "2": "cdk-data", "3": "jline-reader", "4": "commons-valid.", "5": "commons-codec", "6": "spotify-web-api", "7": "commons-text", "8": "dyn4j", "9": "jfreechart" }, "KLoC": { "0"...
[ { "Subject Project": { "0": "commons-cli", "1": "joda-money", "2": "cdk-data", "3": "jline-reader", "4": "commons-valid.", "5": "commons-codec", "6": "spotify-web-api", "7": "commons-text", "8": "dyn4j", "9": "jfreechart" }, "KLoC": { "0"...
[]
[ { "figure_index": 0, "caption": "", "reason": "missing_caption" }, { "figure_index": 1, "caption": "", "reason": "missing_caption" }, { "figure_index": 2, "caption": "", "reason": "missing_caption" }, { "figure_index": 3, "caption": "Figure 1: Illustrating the...
true
null
[ "Figure 4: Ripples of mutations - Sankey diagram per project", "Figure 5: Surviving mutants that are killable by current tests", "Figure 6: Sankey diagram aggregated by mutation operators" ]
neurojit
4ae34bedae4ec72336d172f8e3438182486d5349bc033e0a8664912511a2069c
[ "Table 2: Summarized Statistics of Dataset", "Table 3: Average Ratios of Actionable Features within Top 5 Contribution Rankings of LIME Explanations" ]
[ { "Project": { "0": "ActiveMQ", "1": "Camel", "2": "Flink", "3": "Groovy", "4": "Cassandra", "5": "HBase", "6": "Hive", "7": "Ignite", "8": "" }, "# Defective Commits": { "0": "285 (11.79%)", "1": "481 (7.88%)", "2": "334 (11.29%)",...
[ { "Project": { "0": "ActiveMQ", "1": "Camel", "2": "Flink", "3": "Groovy", "4": "Cassandra", "5": "HBase", "6": "Hive", "7": "Ignite", "8": "" }, "# Defective Commits": { "0": "<obf> (<obf>%)", "1": "<obf> (<obf>%)", "2": "<obf> (<o...
[ { "table_index": 0, "caption": "Table 1: Commit Understandability Features of NeuroJIT and Their Evidence", "locations": [ { "page_no": 3, "bbox": { "l": 52.84231185913086, "t": 686.4656982421875, "r": 558.8575439453125, "b": 207.694030761718...
[ { "figure_index": 0, "caption": "", "reason": "missing_caption" }, { "figure_index": 1, "caption": "", "reason": "missing_caption" }, { "figure_index": 2, "caption": "", "reason": "missing_caption" }, { "figure_index": 3, "caption": "Figure 1: Process of Colle...
true
null
[ "Figure 2: Correlations of Features in Each Group", "Figure 4: Predictive Power of Understandability Features", "Figure 5: Set Relationships between True Positives Predicted by Understandability Models and Baseline Models", "Figure 7: Performance Comparison between Baseline Models and Baseline+Understandabili...
pmsat
bfc4d5969e9205b5ddc1f1d0efe3a7316a1d09546a29d92765a0408ede91c11f
[ "Table 2: Statistics of inferring ping-pong server with different 𝑛 as parameter, with 𝑛 π‘Ÿπ‘’π‘Žπ‘β„Ž dominant reachable states, number of glitches and different statistics of frequencies (fr.) for glitched 𝛿 𝑔 and dominant 𝛿 transitions.", "Table 4: Statistics of inferring the APC with different 𝑛 as paramete...
[ { "𝑛": { "0": 3, "1": 4, "2": 5, "3": 6, "4": 7 }, "𝑛 π‘Ÿπ‘’π‘Žπ‘β„Ž": { "0": 3, "1": 4, "2": 4, "3": 4, "4": 4 }, "# Glitches": { "0": 31, "1": 5, "2": 4, "3": 4, "4": 4 }, "Mean 𝛿 𝑔 fr.": { "...
[ { "𝑛": { "0": "<obf>", "1": "<obf>", "2": "<obf>", "3": "<obf>", "4": "<obf>" }, "𝑛 π‘Ÿπ‘’π‘Žπ‘β„Ž": { "0": "<obf>", "1": "<obf>", "2": "<obf>", "3": "<obf>", "4": "<obf>" }, "# Glitches": { "0": "<obf>", "1": "<obf>", "2...
[ { "table_index": 0, "caption": "Table 1: Summary of variables for inferring automata with states S from traces T with inputs I and outputs O .", "locations": [ { "page_no": 3, "bbox": { "l": 331.2044982910156, "t": 675.6135177612305, "r": 544.365112304...
[ { "figure_index": 0, "caption": "", "reason": "missing_caption" }, { "figure_index": 1, "caption": "", "reason": "missing_caption" }, { "figure_index": 2, "caption": "", "reason": "missing_caption" }, { "figure_index": 3, "caption": "(a) Moore machine of simpl...
true
null
[ "(a) Inferred stochastic Moore machine with 𝑛 = 3 states of ping-pong server with 31 glitches", "(b) Inferred stochastic Moore machine with 𝑛 = 4 states of ping-pong server with 5 glitches", "(a) Percentage of correctly inferred models based on 𝑛 and the output alphabet size, 50 experiments over all differen...
ppt4j
badd52654f8b11f26ce4c18911586e332e4c5198a8d67c24424b2ee4e686f2b6
[]
[]
[]
[ { "table_index": 0, "caption": "Table 1: List of vulnerabilities selected from Vul4J [4]", "locations": [ { "page_no": 7, "bbox": { "l": 79.1219711303711, "t": 686.5258712768555, "r": 532.426025390625, "b": 491.026611328125, "coord_...
[ { "figure_index": 0, "caption": "", "reason": "missing_caption" }, { "figure_index": 1, "caption": "Figure 2: Overall approach of Ppt4J", "reason": "no_data" }, { "figure_index": 2, "caption": "Figure 3: The railroad diagram that illustrates feature types selected in our appr...
true
null
[ "Figure 9: Test results for different variants of Ppt4J" ]
provenfix
ee880878ac19b130410036508d13ba768bffa07f9ed613b425dc44b195444926
[ "Table 2. Experimental results for analyzing 10 C projects, comparing with Infer-v1.1.0. Columns #NPD , #ML and #RL record the numbers of null pointer dereferences, memory leaks, and resource leaks, respectively. The numbers of false positives found by Infer and more true positives found by ProveNFix are represente...
[ { "Project.Project": { "0": "Swoole(a4256e4)", "1": "lxc(72cc48f)", "2": "WavPack(22977b2)", "3": "flex(d3de49f)", "4": "p11-kit", "5": "x264(d4099dd)", "6": "recutils-1.8", "7": "inetutils-1.9.4", "8": "snort-2.9.13", "9": "grub(c6b9a0a)", "10":...
[ { "Project.Project": { "0": "Swoole(a4256e4)", "1": "lxc(72cc48f)", "2": "WavPack(22977b2)", "3": "flex(d3de49f)", "4": "p11-kit", "5": "x264(d4099dd)", "6": "recutils-1.8", "7": "inetutils-1.9.4", "8": "snort-2.9.13", "9": "grub(c6b9a0a)", "10":...
[ { "table_index": 0, "caption": "Table 1. Summary of the Annotated API Specifications.", "locations": [ { "page_no": 15, "bbox": { "l": 51.59090042114258, "t": 613.3089370727539, "r": 431.8812561035156, "b": 533.3761444091797, "coord...
[ { "figure_index": 0, "caption": "", "reason": "missing_caption" }, { "figure_index": 1, "caption": "", "reason": "missing_caption" }, { "figure_index": 2, "caption": "Fig. 8. System Overview.", "reason": "no_data" } ]
true
null
[]
roam
920b33d9afb339f2cdabbf036f0ecb16d0ad3a72f7ddfd7c12fef22dc7d67f4c
[]
[]
[]
[ { "table_index": 0, "caption": "Table 1. Bug Report Step Information", "locations": [ { "page_no": 10, "bbox": { "l": 106.13656616210938, "t": 613.2817459106445, "r": 379.4844970703125, "b": 553.7373046875, "coord_origin": "BOTTOMLE...
[ { "figure_index": 0, "caption": "", "reason": "missing_caption" }, { "figure_index": 1, "caption": "", "reason": "missing_caption" }, { "figure_index": 2, "caption": "Fig. 2. The Actual UI Interactions to Replay the Steps", "reason": "no_data" }, { "figure_index":...
true
null
[ "Fig. 5. Reproduction Rate of Each Approach on Bug Reports with Missing Steps" ]
rust
75ad4fb7248428e553c4d68b450e13a7e71944dc1157c829d1841d42c01cbf6c
[ "Table 1: Resolution accuracy.", "Table 2: Summary of RUF usage.", "Table 3: Summary of RUF impacts. The table shows how package versions are impacted by different types of RUF and through different dependencies.", "Table 5: RUF impact mitigation results. Applying our mitigation strategy, 90% of package versi...
[ { "Dataset Type": { "0": "Random", "1": "Popular", "2": "Mostdep" }, "Tree Accuracy": { "0": "99.39%", "1": "99.50%", "2": "97.04%" }, "Precision": { "0": "99.76%", "1": "99.99%", "2": "99.96%" }, "Recall": { "0": "98.88%", ...
[ { "Dataset Type": { "0": "Random", "1": "Popular", "2": "Mostdep" }, "Tree Accuracy": { "0": "<obf>%", "1": "<obf>%", "2": "<obf>%" }, "Precision": { "0": "<obf>%", "1": "<obf>%", "2": "<obf>%" }, "Recall": { "0": "<obf>%", ...
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[ { "figure_index": 0, "caption": "", "reason": "missing_caption" }, { "figure_index": 1, "caption": "Figure 1: Architecture overview of our work.", "reason": "no_data" }, { "figure_index": 2, "caption": "Figure 5: Dependency influence example. While A doesn't change its depend...
true
null
[]
unimocg
b4d59afd1da2264e42c3b3e3f6f592cb7710b5da83d176280e5cfdb037ceaf51
[ "Table 1: Soundness of call-graphs for different JVM features", "Table 2: Soundness of Unimocg's call-graph algorithms" ]
[ { "Feature": { "0": "Non-virtual Calls", "1": "Virtual Calls", "2": "Types", "3": "Static Initializer", "4": "Java 8 Interfaces", "5": "Unsafe", "6": "Class.forName", "7": "Sign. Polymorph.", "8": "Java 9+", "9": "Non-Java", "10": "MethodHandle",...
[ { "Feature": { "0": "Non-virtual Calls", "1": "Virtual Calls", "2": "Types", "3": "Static Initializer", "4": "Java 8 Interfaces", "5": "Unsafe", "6": "Class.forName", "7": "Sign. Polymorph.", "8": "Java 9+", "9": "Non-Java", "10": "MethodHandle",...
[ { "table_index": 2, "caption": "Table 3: Precision and Performance of different Call-Graph Algorithms", "locations": [ { "page_no": 9, "bbox": { "l": 52.863487243652344, "t": 686.4446792602539, "r": 558.82080078125, "b": 567.8512573242188, ...
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[]
urcrat
95afa90e525546cd32f888f7b824f60d911a5b22249b8eb27a0996f246a50eb8
[ "Table 1: Benchmark programs" ]
[ { "Name": { "0": "bc-1.07.1", "1": "binn-3.0 βˆ—βˆ—", "2": "brotli-1.0.9 βˆ—βˆ—", "3": "cflow-1.7", "4": "compton βˆ—", "5": "cpio-2.14", "6": "diffutils-3.10", "7": "enscript-1.6.6", "8": "findutils-4.9.0", "9": "gawk-5.2.2", "10": "glpk-5.0", "11":...
[ { "Name": { "0": "bc-1.07.1", "1": "binn-3.0 βˆ—βˆ—", "2": "brotli-1.0.9 βˆ—βˆ—", "3": "cflow-1.7", "4": "compton βˆ—", "5": "cpio-2.14", "6": "diffutils-3.10", "7": "enscript-1.6.6", "8": "findutils-4.9.0", "9": "gawk-5.2.2", "10": "glpk-5.0", "11":...
[]
[ { "figure_index": 0, "caption": "", "reason": "missing_caption" }, { "figure_index": 1, "caption": "", "reason": "missing_caption" }, { "figure_index": 2, "caption": "", "reason": "missing_caption" }, { "figure_index": 3, "caption": "Figure 1: The workflow of ...
true
null
[ "Figure 2: Urcrat execution time" ]
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