ISSTA 2022

72 papers accepted.

Updated on 2023-09-08.

You can find the lastest information here.

jTrans: jump-aware transformer for binary code similarity detection.

FDG: a precise measurement of fault diagnosability gain of test cases.

TeLL: log level suggestions via modeling multi-level code block information.

An extensive study on pre-trained models for program understanding and generation.

Metamorphic relations via relaxations: an approach to obtain oracles for action-policy testing.

Hunting bugs with accelerated optimal graph vertex matching.

Using pre-trained language models to resolve textual and semantic merge conflicts (experience paper).

Combining solution reuse and bound tightening for efficient analysis of evolving systems.

On the use of evaluation measures for defect prediction studies.

Evolution-aware detection of order-dependent flaky tests.

ε-weakened robustness of deep neural networks.

Simple techniques work surprisingly well for neural network test prioritization and active learning (replicability study).

Improving cross-platform binary analysis using representation learning via graph alignment.

BET: black-box efficient testing for convolutional neural networks.

DocTer: documentation-guided fuzzing for testing deep learning API functions.

ASRTest: automated testing for deep-neural-network-driven speech recognition systems.

AEON: a method for automatic evaluation of NLP test cases.

Human-in-the-loop oracle learning for semantic bugs in string processing programs.

HybridRepair: towards annotation-efficient repair for deep learning models.

Cross-lingual transfer learning for statistical type inference.

Unicorn: detect runtime errors in time-series databases with hybrid input synthesis.

On the use of mutation analysis for evaluating student test suite quality.

Test mimicry to assess the exploitability of library vulnerabilities.

Automated test generation for REST APIs: no time to rest yet.

Finding bugs in Gremlin-based graph database systems via Randomized differential testing.

RegMiner: towards constructing a large regression dataset from code evolution history.

One step further: evaluating interpreters using metamorphic testing.

SnapFuzz: high-throughput fuzzing of network applications.

Almost correct invariants: synthesizing inductive invariants by fuzzing proofs.

SLIME: program-sensitive energy allocation for fuzzing.

MDPFuzz: testing models solving Markov decision processes.

TensileFuzz: facilitating seed input generation in fuzzing via string constraint solving.

PrIntFuzz: fuzzing Linux drivers via automated virtual device simulation.

Efficient greybox fuzzing of applications in Linux-based IoT devices via enhanced user-mode emulation.

Understanding device integration bugs in smart home system.

A large-scale empirical analysis of the vulnerabilities introduced by third-party components in IoT firmware.

Deadlock prediction via generalized dependency.

Automated testing of image captioning systems.

LiRTest: augmenting LiDAR point clouds for automated testing of autonomous driving systems.

Detecting multi-sensor fusion errors in advanced driver-assistance systems.

Precise and efficient atomicity violation detection for interrupt-driven programs via staged path pruning.

Path-sensitive code embedding via contrastive learning for software vulnerability detection.

A large-scale study of usability criteria addressed by static analysis tools.

An empirical study on the effectiveness of static C code analyzers for vulnerability detection.

Testing Dafny (experience paper).

Combining static analysis error traces with dynamic symbolic execution (experience paper).

The raise of machine learning hyperparameter constraints in Python code.

Detecting and fixing data loss issues in Android apps.

Automatically detecting API-induced compatibility issues in Android apps: a comparative analysis (replicability study).

NCScope: hardware-assisted analyzer for native code in Android apps.

Detecting resource utilization bugs induced by variant lifecycles in Android.

Patch correctness assessment in automated program repair based on the impact of patches on production and test code.

ATR: template-based repair for Alloy specifications.

CIRCLE: continual repair across programming languages.

Program vulnerability repair via inductive inference.

WASAI: uncovering vulnerabilities in Wasm smart contracts.

Finding permission bugs in smart contracts with role mining.

Park: accelerating smart contract vulnerability detection via parallel-fork symbolic execution.

SmartDagger: a bytecode-based static analysis approach for detecting cross-contract vulnerability.

ATUA: an update-driven app testing tool.

Automatic generation of smoke test suites for kubernetes.

ESBMC-CHERI: towards verification of C programs for CHERI platforms with ESBMC.

ESBMC-Jimple: verifying Kotlin programs via jimple intermediate representation.

Faster mutation analysis with MeMu.

iFixDataloss: a tool for detecting and fixing data loss issues in Android apps.

Maestro: a platform for benchmarking automatic program repair tools on software vulnerabilities.

Pytest-Smell: a smell detection tool for Python unit tests.

QMutPy: a mutation testing tool for Quantum algorithms and applications in Qiskit.

SpecChecker-ISA: a data sharing analyzer for interrupt-driven embedded software.

UniRLTest: universal platform-independent testing with reinforcement learning via image understanding.