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  1. Deep Learning (DL) models to analyze source code have shown immense promise during the past few years. More recently, self-supervised pre-training has gained traction for learning generic code representations valuable for many downstream SE tasks, such as clone and bug detection. While previous work successfully learned from different code abstractions (e.g., token, AST, graph), we argue that it is also essential to factor in how developers code day-to-day for general-purpose representation learning. On the one hand, human developers tend to write repetitive programs referencing existing code snippets from the current codebase or online resources (e.g., Stack Overflow website) rather than implementing functions from scratch; such behaviors result in a vast number of code clones. In contrast, a deviant clone by mistake might trigger malicious program behaviors. Thus, as a proxy to incorporate developers' coding behavior into the pre-training scheme, we propose to include code clones and their deviants. In particular, we propose CONCORD, a self-supervised, contrastive learning strategy to place benign clones closer in the representation space while moving deviants further apart. We show that CONCORD's clone-aware contrastive learning drastically reduces the need for expensive pre-training resources while improving the performance of downstream SE tasks. We also empirically demonstrate that CONCORD can improve existing pre-trained models to learn better representations that consequently become more efficient in both identifying semantically equivalent programs and differentiating buggy from non-buggy code. 
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    Free, publicly-accessible full text available July 12, 2024
  2. A literature review revealed that students learning computational thinking via Scratch often require substantial teacher support. We surveyed grade 6-9 teachers to learn their perceptions of student engagement with computational thinking (CT) and how well their needs are met by existing CT learning systems. The results led us to extend the trend of balancing Scratch’s agency with structure to better serve learners and reduce burden on teachers aiming to learn and teach CT. In this paper, we review architecture and implementation strategies developed to integrate Parsons Programming Puzzles (PPPs) with Scratch, and then analyze their effects on adults, who crucially influence the education of their children. The results from our pilot study suggest PPPs catalyze CT motivation, reduce extraneous cognitive load, and increase learning efficiency without jeopardizing performance on transfer tasks. 
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  3. Self-driving cars and trucks, autonomous vehicles (AVs), should not be accepted by regulatory bodies and the public until they have much higher confidence in their safety and reliability --- which can most practically and convincingly be achieved by testing. But existing testing methods are inadequate for checking the end-to-end behaviors of AV controllers against complex, real-world corner cases involving interactions with multiple independent agents such as pedestrians and human-driven vehicles. While test-driving AVs on streets and highways fails to capture many rare events, existing simulation-based testing methods mainly focus on simple scenarios and do not scale well for complex driving situations that require sophisticated awareness of the surroundings. To address these limitations, we propose a new fuzz testing technique, called AutoFuzz, which can leverage widely-used AV simulators' API grammars to generate semantically and temporally valid complex driving scenarios (sequences of scenes). To efficiently search for traffic violations-inducing scenarios in a large search space, we propose a constrained neural network (NN) evolutionary search method to optimize AutoFuzz. Evaluation of our prototype on one state-of-the-art learning-based controller, two rule-based controllers, and one industrial-grade controller in five scenarios shows that AutoFuzz efficiently finds hundreds of traffic violations in high-fidelity simulation environments. For each scenario, AutoFuzz can find on average 10-39% more unique traffic violations than the best-performing baseline method. Further, fine-tuning the learning-based controller with the traffic violations found by AutoFuzz successfully reduced the traffic violations found in the new version of the AV controller software. 
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  4. To investigate learning system elements and progressions that affect computational thinking (CT) learning in block-based environments, we developed a Parsons Programming Puzzle (PPP) module within Scratch with scaffolding customized via a novel Blockly grammar. By varying the presentation and types of feedback encountered between- and within-subjects in a study of 579 adults, we identified features and scaffolding strategies that yield manageable cognitive load (CL), improved CT learning efficiency, and increased motivation, for a general populace. Findings indicate: 1) PPPs with feedback induce lowest CL; 2) an isolated palette, correctness feedback, and fading correctness feedback increase learning efficiency; 3) fading scaffolding can increase CT motivation. We analyze 12 conditions to provide insight to those developing block-based PPP systems with the aim to advance equitable CT education for all. 
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  5. Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts. This becomes even more important in today's software ecosystem, where vulnerable code can flow easily and unwittingly within and across software repositories like GitHub. Across such millions of lines of code, traditional static and dynamic approaches struggle to scale. Although existing machine-learning-based approaches look promising in such a setting, most work detects vulnerable code at a higher granularity – at the method or file level. Thus, developers still need to inspect a significant amount of code to locate the vulnerable statement(s) that need to be fixed. This paper presents Velvet, a novel ensemble learning approach to locate vulnerable statements. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph and effectively understand code semantics and vulnerable patterns. To study Velvet's effectiveness, we use an off-the-shelf synthetic dataset and a recently published real-world dataset. In the static analysis setting, where vulnerable functions are not detected in advance, Velvet achieves 4.5× better performance than the baseline static analyzers on the real-world data. For the isolated vulnerability localization task, where we assume the vulnerability of a function is known while the specific vulnerable statement is unknown, we compare Velvet with several neural networks that also attend to local and global context of code. Velvet achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively, outperforming the baseline deep learning models by 5.3-29.0%. 
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  6. Using a design thinking approach, we surveyed and interviewed grade 6-9 teachers on their experience with Scratch and Parsons Programming Puzzles (PPP). The results lead us to extend Scratch with gameful PPP functionality focused on individual computational thinking (CT) concepts. In this paper, we vary elements of PPPs presented to 624 adult learners to identify those yielding manageable cognitive load (CL), and maximum CT motivation and learning efficiency, for a general populace. Findings indicate PPPs with feedback and without distractors limit CL, those with feedback produce highest CT motivation, and those with an isolated block palette and without distractors produce highest CT learning efficiency. We analyze study data across nine conditions to offer insight to those developing PPP systems with the aim to advance equitable CT education for all. 
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  7. Enterprise software updates depend on the interaction between user and developer organizations. This interaction becomes especially complex when a single developer organization writes software that services hundreds of different user organizations. Miscommunication during patching and deployment efforts lead to insecure or malfunctioning software installations. While developers oversee the code, the update process starts and ends outside their control. Since developer test suites may fail to capture buggy behavior finding and fixing these bugs starts with user generated bug reports and 3rd party disclosures. The process ends when the fixed code is deployed in production. Any friction between user, and developer results in a delay patching critical bugs. Two common causes for friction are a failure to replicate user specific circumstances that cause buggy behavior and incompatible software releases that break critical functionality. Existing test generation techniques are insufficient. They fail to test candidate patches for post-deployment bugs and to test whether the new release adversely effects customer workloads. With existing test generation and deployment techniques, users can't choose (nor validate) compatible portions of new versions and retain their previous version's functionality. We present two new technologies to alleviate this friction. First, Test Generation for Ad Hoc Circumstances transforms buggy executions into test cases. Second, Binary Patch Decomposition allows users to select the compatible pieces of update releases. By sharing specific context around buggy behavior and developers can create specific test cases that demonstrate if their fixes are appropriate. When fixes are distributed by including extra context users can incorporate only updates that guarantee compatibility between buggy and fixed versions. We use change analysis in combination with binary rewriting to transform the old executable and buggy execution into a test case including the developer's prospective changes that let us generate and run targeted tests for the candidate patch. We also provide analogous support to users, to selectively validate and patch their production environments with only the desired bug-fixes from new version releases. This paper presents a new patching workflow that allows developers to validate prospective patches and users to select which updates they would like to apply, along with two new technologies that make it possible. We demonstrate our technique constructs tests cases more effectively and more efficiently than traditional test case generation on a collection of real world bugs compared to traditional test generation techniques, and provides the ability for flexible updates in real world scenarios. 
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  8. null (Ed.)
    Machine learning-based malware detection systems are often vulnerable to evasion attacks, in which a malware developer manipulates their malicious software such that it is misclassified as benign. Such software hides some properties of the real class or adopts some properties of a different class by applying small perturbations. A special case of evasive malware hides by repackaging a bonafide benign mobile app to contain malware in addition to the original functionality of the app, thus retaining most of the benign properties of the original app. We present a novel malware detection system based on metamorphic testing principles that can detect such benign-seeming malware apps. We apply metamorphic testing to the feature representation of the mobile app, rather than to the app itself. That is, the source input is the original feature vector for the app and the derived input is that vector with selected features removed. If the app was originally classified benign, and is indeed benign, the output for the source and derived inputs should be the same class, i.e., benign, but if they differ, then the app is exposed as (likely) malware. Malware apps originally classified as malware should retain that classification, since only features prevalent in benign apps are removed. This approach enables the machine learning model to classify repackaged malware with reasonably few false negatives and false positives. Our training pipeline is simpler than many existing ML-based malware detection methods, as the network is trained end-to-end to jointly learn appropriate features and to perform classification. We pre-trained our classifier model on 3 million apps collected from the widely-used AndroZoo dataset. 1 We perform an extensive study on other publicly available datasets to show our approach’s effectiveness in detecting repackaged malware with more than 94% accuracy, 0.98 precision, 0.95 recall, and 0.96 F1 score. 
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  9. null (Ed.)
    We surveyed grade 6-9 teachers to learn teacher perceptions of student engagement with computational thinking (CT) and how well their needs are met by existing CT learning systems. The results and a literature review lead us to extend the trend of balancing Scratch’s agency with structure to better serve learners and reduce burden on teachers aiming to learn and teach CT. In this paper, we integrate Parsons Programming Puzzles (PPPs) with Scratch and analyze the effects on adults, who crucially influence the education of their children. The results from our small pilot study suggest PPPs catalyze CT motivation, reduce extraneous cognitive load, and increase learning efficiency without jeopardizing performance on transfer tasks. 
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  10. Decompiling binary executables to high-level code is an important step in reverse engineering scenarios, such as malware analysis and legacy code maintenance. However, the generated high-level code is difficult to understand since the original variable names are lost. In this paper, we leverage transformer models to reconstruct the original variable names from decompiled code. Inherent differences between code and natural language present certain challenges in applying conventional transformer-based architectures to variable name recovery. We propose DIRECT, a novel transformer-based architecture customized specifically for the task at hand. We evaluate our model on a dataset of decompiled functions and find that DIRECT outperforms the previous state-of-the-art model by up to 20%. We also present ablation studies evaluating the impact of each of our modifications. We make the source code of DIRECT available to encourage reproducible research. 
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