id stringlengths 3 9 | tables_to_rep listlengths 1 6 | tables_gt_data listlengths 1 6 | tables_obf_data listlengths 1 6 | figures_to_rep listlengths 0 7 | figures_to_rep_image images listlengths 0 7 | figure_gt_data listlengths 0 7 | figure_obf_data listlengths 0 7 |
|---|---|---|---|---|---|---|---|
mutation | [
"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\": <obf>, \"1\": <obf>, \"2\": <obf>... | [
"RQ2: How does mutation execution influence test execution behaviors?",
"Figure 5: Surviving mutants that are killable by current tests",
"Figure 6: Sankey diagram aggregated by mutation operators"
] | [
{
"data": [
{
"project": "cdk-data",
"start": "E",
"infection": [
{
"label": "NI",
"value": 32.7
},
{
"label": "I",
"value": 67.3
}
],
"propagation": [
{
"l... | [
"{\"data\": [{\"project\": \"cdk-data\", \"start\": \"E\", \"infection\": [{\"label\": \"NI\", \"value\": <obf>}, {\"label\": \"I\", \"value\": <obf>}], \"propagation\": [{\"label\": \"P\", \"value\": <obf>}, {\"label\": \"FL\", \"value\": <obf>}], \"revealability\": [{\"label\": \"P\", \"value\": <obf>}, {\"label\... | |
provenfix | [
"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-<obf>\", \"7\": \"inetutils-<obf>\", \"8\": \"snort-<obf>\", \"9\": \"grub(c6b9a0a)\", \"10\": \"Total\"}, \"kLoC.kLoC\"... | [] | [] | [] | |
urcrat | [
"Table 1: Benchmark programs"
] | [
{
"Name": {
"0": "bc-1.07.1",
"1": "binn-3.0 ββ",
"2": "brotli-1.0.9 ββ",
"3": "cflow-1.7",
"4": "compton β",
"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-<obf>\", \"1\": \"binn-<obf> \\u2217\\u2217\", \"2\": \"brotli-<obf> \\u2217\\u2217\", \"3\": \"cflow-<obf>\", \"4\": \"compton \\u2217\", \"5\": \"cpio-<obf>\", \"6\": \"diffutils-<obf>\", \"7\": \"enscript-<obf>\", \"8\": \"findutils-<obf>\", \"9\": \"gawk-<obf>\", \"10\": \"glpk-<obf>\",... | [
"Figure 2: Urcrat execution time"
] | [
{
"data": [
{
"Rust LOC": 5000,
"Time (s)": 0.11
},
{
"Rust LOC": 10000,
"Time (s)": 0.45
},
{
"Rust LOC": 12000,
"Time (s)": 0.55
},
{
"Rust LOC": 15000,
"Time (s)": 0.7000000000000001
},
{
... | [
"{\"data\": [{\"Rust LOC\": <obf>, \"Time (s)\": <obf>}, {\"Rust LOC\": <obf>, \"Time (s)\": <obf>}, {\"Rust LOC\": <obf>, \"Time (s)\": <obf>}, {\"Rust LOC\": <obf>, \"Time (s)\": <obf>}, {\"Rust LOC\": <obf>, \"Time (s)\": <obf>}, {\"Rust LOC\": <obf>, \"Time (s)\": <obf>}, {\"Rust LOC\": <obf>, \"Time (s)\": <ob... | |
crossover | [
"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 (<obf>) [<obf>]\", \"1\": \"Apache BCEL (<obf>) [<obf>]\", \"2\": \"Google Closure (v20230502) [<obf>]\", \"3\": \"Apache Maven (<obf>) [<obf>]\", \"4\": \"OpenJDK Nashorn (<obf>) [<obf>]\", \"5\": \"Mozilla Rhino (<obf>) [<obf>]\", \"6\": \"Apache Tomcat (<obf>) [<obf>]\"}, \"Fo... | [] | [] | [] | |
dypybench | [
"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./project\", \"12\": \"Min.\"... | [
"Fig. 4. Number of test cases vs. execution time for each projects.",
"Fig. 5. Venn diagrams of edges, callers, and callees in dynamic call graphs (left) and static call graphs (right).",
"Fig. 6. Accuracy of LExecutor [59] model trained on DyPyBench-based data.",
"Fig. 8. Number of extracted sequences per pr... | [
{
"data": [
{
"library": "delegator",
"number_of_test_cases": 1,
"execution_time_seconds": 1.1
},
{
"library": "akshare",
"number_of_test_cases": 3,
"execution_time_seconds": 2.6
},
{
"library": "blinker",
"number_of_t... | [
"{\"data\": [{\"library\": \"delegator\", \"number_of_test_cases\": <obf>, \"execution_time_seconds\": <obf>}, {\"library\": \"akshare\", \"number_of_test_cases\": <obf>, \"execution_time_seconds\": <obf>}, {\"library\": \"blinker\", \"number_of_test_cases\": <obf>, \"execution_time_seconds\": <obf>}, {\"library\":... | |
unimocg | [
"Table 1: Soundness of call-graphs for different JVM features",
"Table 2: Soundness of Unimocg's call-graph algorithms",
"Table 3: Precision and Performance of different Call-Graph Algorithms",
"Table 4: Field Immutability Results for OpenJDK"
] | [
{
"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 <obf> Interfaces",
"5": "Unsafe",
"6": "Class.forName",
"7": "Sign. Polymorph.",
"8": "Java <obf>+",
"9": "Non-Java",
"10": "Method... | [] | [] | [] | |
bloat | [
"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": "",
"4": "Data Analysis",
"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\": \"\", \"4\": \"Data Analysis\", \"5\": \"\"}, \"Operation\": {\"0\": \"Initial dataset of Python GitHub projects\", \"1\": \"Filtering inaccessible projects\", \"2\": \"Dependency resolution\", \"3\": \"Partial call graph construction\", \"4\... | [
"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... | [
{
"data": [
{
"entity_type": "Package",
"bloated_loc": {
"median": 28,
"q1": 6,
"q3": 53,
"mean": 35
},
"bloated_entries": {
"median": 52,
"q1": 33,
"q3": 70,
"mean": 53
}
},
... | [
"{\"data\": [{\"entity_type\": \"Package\", \"bloated_loc\": {\"median\": <obf>, \"q1\": <obf>, \"q3\": <obf>, \"mean\": <obf>}, \"bloated_entries\": {\"median\": <obf>, \"q1\": <obf>, \"q3\": <obf>, \"mean\": <obf>}}, {\"entity_type\": \"File\", \"bloated_loc\": {\"median\": <obf>, \"q1\": <obf>, \"q3\": <obf>, \"... | |
axa | [
"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\": \"<obf> (JS), <obf> (Native)\", \"1\": \"<obf> + <obf>\", \"2\": <obf>}, \"Lattice\": {\"0\": \"<obf> (JS)\", \"1\": \"<obf>+<obf>\", \"2\": <obf>}, \"Solver\": {\"0\": <obf>, \"1\": <obf>, \"2\": <obf>}, \"Connector\... | [] | [] | [] | |
roam | [
"Table 1. Bug Report Step Information",
"Table 2. The Match Accuracy Results on Bug Reports (Shown as a Percent)",
"Table 3. Reproduction Rate of Each Approach (Shown as a Percent)",
"Table 4. The Running Time Results of Each Approach (Shown in Seconds)",
"Table 5. The Match Accuracy and Reproduction Rate f... | [
{
"": {
"0": "Provided Steps",
"1": "Total Steps",
"2": "Missing Steps"
},
"Average": {
"0": 2.9,
"1": 4.3,
"2": 2.4
},
"Median": {
"0": 3,
"1": 3,
"2": 1
},
"Minimum": {
"0": 1,
"1": 1,
"2": 1
},
"Maximum": ... | [
"{\"\": {\"0\": \"Provided Steps\", \"1\": \"Total Steps\", \"2\": \"Missing Steps\"}, \"Average\": {\"0\": <obf>, \"1\": <obf>, \"2\": <obf>}, \"Median\": {\"0\": <obf>, \"1\": <obf>, \"2\": <obf>}, \"Minimum\": {\"0\": <obf>, \"1\": <obf>, \"2\": <obf>}, \"Maximum\": {\"0\": <obf>, \"1\": <obf>, \"2\": <obf>}}",
... | [
"Fig. 5. Reproduction Rate of Each Approach on Bug Reports with Missing Steps"
] | [
{
"data": [
{
"Minimum Number of Missing Steps in a Bug Report": 0,
"Roam": 94,
"ReproBot": 70,
"ReCDroid": 29,
"Yakusu": 8
},
{
"Minimum Number of Missing Steps in a Bug Report": 1,
"Roam": 93,
"ReproBot": 63,
"ReCDroid":... | [
"{\"data\": [{\"Minimum Number of Missing Steps in a Bug Report\": <obf>, \"Roam\": <obf>, \"ReproBot\": <obf>, \"ReCDroid\": <obf>, \"Yakusu\": <obf>}, {\"Minimum Number of Missing Steps in a Bug Report\": <obf>, \"Roam\": <obf>, \"ReproBot\": <obf>, \"ReCDroid\": <obf>, \"Yakusu\": <obf>}, {\"Minimum Number of Mi... | |
npetest | [
"Table 1: Benchmarks collected from prior works [3, 21-24]. Projectrep : # of reproducible projects in our experimental environment. NPE: # of known NPEs from reproducible projects Projectrep . NPEtest : # of NPEs occured in a test case itself. NPE outside : # of NPEs triggered outside of the target project (e.g., ... | [
{
"Source": {
"0": "NPEX [3]",
"1": "Genesis [23]",
"2": "Bears [21]",
"3": "BugSwarm [22]",
"4": "Defects4J [24]",
"5": "Total"
},
"Project": {
"0": 59,
"1": 16,
"2": 18,
"3": 76,
"4": 26,
"5": 195
},
"Project rep": {
... | [
"{\"Source\": {\"0\": \"NPEX [<obf>]\", \"1\": \"Genesis [<obf>]\", \"2\": \"Bears [<obf>]\", \"3\": \"BugSwarm [<obf>]\", \"4\": \"Defects4J [<obf>]\", \"5\": \"Total\"}, \"Project\": {\"0\": <obf>, \"1\": <obf>, \"2\": <obf>, \"3\": <obf>, \"4\": <obf>, \"5\": <obf>}, \"Project rep\": {\"0\": <obf>, \"1\": <obf>,... | [
"Figure 4: A Venn diagram illustrating the number of unique NPEs found by each tool.",
"Figure 5: A graph for an average line coverage of EvoSuite and EvoSuite π·ππ on each benchmark suite.",
"Figure 6: A Venn diagram illustrating the number of unique NPEs found by each tool on an industrial project."
] | [
{
"data": [
{
"NPETest_only": 12,
"EvoSuite_only": 1,
"Randoop_only": 0,
"NPETest_EvoSuite_intersection": 35,
"NPETest_Randoop_intersection": 2,
"EvoSuite_Randoop_intersection": 23,
"NPETest_EvoSuite_Randoop_intersection": 0
}
],
"descr... | [
"{\"data\": [{\"NPETest_only\": <obf>, \"EvoSuite_only\": <obf>, \"Randoop_only\": <obf>, \"NPETest_EvoSuite_intersection\": <obf>, \"NPETest_Randoop_intersection\": <obf>, \"EvoSuite_Randoop_intersection\": <obf>, \"NPETest_EvoSuite_Randoop_intersection\": <obf>}], \"description\": \"The JSON object represents the... | |
lasapp | [
"Table 1: Lines of code needed to add LASAPP support for various PPLs and probability distribution back-ends.",
"Table 2: Summary table of evaluation results."
] | [
{
"Back-end / PPL": {
"0": "Language Server PyMC [51]",
"1": "torch.distributions [48] Pyro [8]",
"2": "",
"3": "Language Server Distributions.jl [7] Gen [14]",
"4": "Turing [21]"
},
"LOC": {
"0": "1549 281",
"1": 67,
"2": 133,
"3": "2175 240",
... | [
"{\"Back-end / PPL\": {\"0\": \"Language Server PyMC [<obf>]\", \"1\": \"torch.distributions [<obf>] Pyro [<obf>]\", \"2\": \"\", \"3\": \"Language Server Distributions.jl [<obf>] Gen [<obf>]\", \"4\": \"Turing [<obf>]\"}, \"LOC\": {\"0\": \"<obf>\", \"1\": <obf>, \"2\": <obf>, \"3\": \"<obf>\", \"4\": \"<obf>\"}, ... | [] | [] | [] | |
pmsat | [
"Table 1: Summary of variables for inferring automata with states S from traces T with inputs I and outputs O .",
"Table 2: Statistics of inferring ping-pong server with different π as parameter, with π ππππβ dominant reachable states, number of glitches and different statistics of frequencies (fr.) for gli... | [
{
"Eq.": {
"0": "(1)",
"1": "(2)",
"2": "(3)",
"3": "(4)",
"4": "(5)",
"5": "(6)",
"6": "(7)",
"7": "(8)"
},
"Clauses": {
"0": "π ( π ,π )",
"1": "πΏ ( π , π, π β² )",
"2": "π ( π‘, π, π )",
"3": "π ( π‘, π, π ) β¨ π ( π ,οΏ½... | [
{
"Eq.": {
"0": "(<obf>)",
"1": "(<obf>)",
"2": "(<obf>)",
"3": "(<obf>)",
"4": "(<obf>)",
"5": "(<obf>)",
"6": "(<obf>)",
"7": "(<obf>)"
},
"Clauses": {
"0": "π ( π ,π )",
"1": "πΏ ( π , π, π β² )",
"2": "π ( π‘, π, π )",
"3"... | [] | [] | [] | |
neurojit | [
"Table 1: Commit Understandability Features of NeuroJIT and Their Evidence",
"Table 2: Summarized Statistics of Dataset",
"Table 3: Average Ratios of Actionable Features within Top 5 Contribution Rankings of LIME Explanations"
] | [
{
"Description": {
"0": "HalsteadVolume (HV) The number of data components in code segment [23].",
"1": "TermEntropy (TE) The relative distribution of unique terms in the source code (i.e., keywords, identifiers, and operators) [46]. TE increases with the uniform distribution of terms, but decreases... | [
{
"Description": {
"0": "HalsteadVolume (HV) The number of data components in code segment [<obf>].",
"1": "TermEntropy (TE) The relative distribution of unique terms in the source code (i.e., keywords, identifiers, and operators) [<obf>]. TE increases with the uniform distribution of terms, but dec... | [
"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"
] | [
{
"data": [
{
"type": "correlation_matrix",
"subgroup": "a",
"label": "baseline features",
"matrix": [
[
"LA",
"NUC",
"LT",
"LD",
"Entropy",
"SEXP",
"EXP",
"AGE",
... | [
"{\"data\": [{\"type\": \"correlation_matrix\", \"subgroup\": \"a\", \"label\": \"baseline features\", \"matrix\": [[\"LA\", \"NUC\", \"LT\", \"LD\", \"Entropy\", \"SEXP\", \"EXP\", \"AGE\", \"NS\"], [\"LA\", null, null, null, null, null, null, null, null, null], [\"NUC\", \"<obf>\", null, null, null, null, null, n... | |
llm | [
"",
"Table 2: Frequencies of n-grams used differently in prompts by professionals and students. For clarity, we only include n-grams used uniquely by one of the two groups, with a frequency difference of more than 2. If multiple n-grams share the same longer n-gram, we report only the superset.",
"Table 3: Summ... | [
{
"": {
"0": "Constant",
"1": "Domain",
"2": "experience",
"3": "Program.",
"4": "experience",
"5": "AI tool",
"6": "familiarity",
"7": "Uses GILT",
"8": "",
"9": "π
2",
"10": "Adj. π
2"
},
"πππππππ π .( 1 )": {
"0": "0 . 41... | [
{
"": {
"0": "Constant",
"1": "Domain",
"2": "experience",
"3": "Program.",
"4": "experience",
"5": "AI tool",
"6": "familiarity",
"7": "Uses GILT",
"8": "",
"9": "π
<obf>",
"10": "Adj. π
<obf>"
},
"πππππππ π .( 1 )": {
"0":... | [
"Figure 4: Participants' report on the importance of GILT features."
] | [
{
"data": [
{
"category": "Prompt",
"Extremely": 14,
"Very": 17,
"Moderately": null,
"Slightly": null,
"Not at all": null
},
{
"category": "Prompt context",
"Extremely": 15,
"Very": 12,
"Moderately": 3,
"Sl... | [
"{\"data\": [{\"category\": \"Prompt\", \"Extremely\": <obf>, \"Very\": <obf>, \"Moderately\": null, \"Slightly\": null, \"Not at all\": null}, {\"category\": \"Prompt context\", \"Extremely\": <obf>, \"Very\": <obf>, \"Moderately\": <obf>, \"Slightly\": null, \"Not at all\": null}, {\"category\": \"Overview\", \"E... |
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