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1. What Types are Needed for Typing Dynamic Objects? A Python-based Empirical Study

   Ke Sun; Sheng Chen; Meng Wang; Dan Hao (November 2023, rogramming Languages and Systems - 21st Asian Symposium)

   Chung-Kil Hur (Ed.)

   Dynamic object-oriented languages, such as Python, Ruby, and Javascript are widely used nowadays. A distinguishing feature of dynamic object-oriented languages is that objects, the fundamental runtime data representation, are highly dynamic, meaning that a single constructor may create objects with different types and objects can evolve freely after their construction. While such dynamism facilitates fast prototyping, it brings many challenges to program understanding. Many type systems have been developed to aid programming understanding, and they adopt various types and techniques to represent and track dynamic objects. However, although many types and techniques have been proposed, it is unclear which one suits real dynamic object usages best. Motivated by this situation, we perform an empirical study on 50 mature Python programs with a focus on object dynamism and object type models. We found that (1) object dynamism is highly prevalent in Python programs, (2) class-based types are not precise to handle dynamic behaviors, as they introduce type errors for 52% of the evaluated polymorphic attributes, (3) typestate-based types, although mostly used in static languages, matches the behaviors of dynamic objects faithfully, and (4) some well-designed but still lightweight techniques for objectbased types, such as argument type separation and recency abstraction can precisely characterize dynamic object behaviors. Those techniques are suitable for building precise but still concise object-based types. 

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2. SnapCheck: Automated Testing for Snap! Programs

   https://doi.org/10.1145/3430665.3456367  

   Wang, Wengran; Zhang, Chenhao; Stahlbauer, Andreas; Fraser, Gordon; Price, Thomas W. (June 2021, ITiCSE '21: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education)

   null (Ed.)

   Programming environments such as Snap, Scratch, and Processing engage learners by allowing them to create programming artifacts such as apps and games, with visual and interactive output. Learning programming with such a media-focused context has been shown to increase retention and success rate. However, assessing these visual, interactive projects requires time and laborious manual effort, and it is therefore difficult to offer automated or real-time feedback to students as they work. In this paper, we introduce SnapCheck, a dynamic testing framework for Snap that enables instructors to author test cases with Condition-Action templates. The goal of SnapCheck is to allow instructors or researchers to author property-based test cases that can automatically assess students' interactive programs with high accuracy. Our evaluation of SnapCheck on 162 code snapshots from a Pong game assignment in an introductory programming course shows that our automated testing framework achieves at least 98% accuracy over all rubric items, showing potentials to use SnapCheck for auto-grading and providing formative feedback to students. 

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3. Role of Spatial Context in Adversarial Robustness for Object Detection

   Saha, Aniruddha (July 2020, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops)

   The benefits of utilizing spatial context in fast object detection algorithms have been studied extensively. Detectors increase inference speed by doing a single forward pass per image which means they implicitly use contextual reasoning for their predictions. However, one can show that an adversary can design adversarial patches which do not overlap with any objects of interest in the scene and exploit con- textual reasoning to fool standard detectors. In this paper, we examine this problem and design category specific adversarial patches which make a widely used object detector like YOLO blind to an attacker chosen object category. We also show that limiting the use of spatial context during object detector training improves robustness to such adversaries. We believe the existence of context based adversarial attacks is concerning since the adversarial patch can affect predictions without being in vicinity of any objects of interest. Hence, defending against such attacks becomes challenging and we urge the research community to give attention to this vulnerability. 

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4. Object Detectors for Construction Resources Using Unmanned Aerial Vehicles

   https://doi.org/10.1061/(ASCE)SC.1943-5576.0000598  

   Mutis, Ivan; Joshi, Virat Arun; Singh, Abhishek (November 2021, Practice Periodical on Structural Design and Construction)

   Project control operations in construction are mostly executed via direct observations and the manual monitoring of progress and performance of construction tasks on the job site. Project engineers move physically within job-site areas to ensure activities are executed as planned. Such physical displacements are error-prone and ineffective in cost and time, particularly in larger construction zones. It is critical to explore new methods and technologies to effectively assist performance control operations by rapidly capturing data from materials and equipment on the job site. Motivated by the ubiquitous use of unmanned aerial vehicles (UAVs) in construction projects and the maturity of computer-vision-based machine-learning (ML) techniques, this research investigates the challenges of object detection—the process of predicting classes of objects (specified construction materials and equipment)—in real time. The study addresses the challenges of data collection and predictions for remote monitoring in project control activities. It uses these two proven and robust technologies by exploring factors that impact the use of UAV aerial images to design and implement object detectors through an analytical conceptualization and a showcase demonstration. The approach sheds light on the applications of deep-learning techniques to access and rapidly identify and classify resources in real-time. It paves the way to shift from costly and time-consuming job-site walkthroughs that are coupled with manual data processing and input to more automated, streamlined operations. The research found that the critical factor to develop object detectors with acceptable levels of accuracy is collecting aerial images with for adequate scales with high frequencies from different positions of the same construction areas. 

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5. On the effectiveness of unified debugging: an extensive study on 16 program repair systems

   https://doi.org/10.1145/3324884.3416566  

   Benton, Samuel; Li, Xia; Lou, Yiling; Zhang, Lingming (December 2020, IEEE/ACM International Conference on Automated Software Engineering)

   null (Ed.)

   Automated debugging techniques, including fault localization and program repair, have been studied for over a decade. However, the only existing connection between fault localization and program repair is that fault localization computes the potential buggy elements for program repair to patch. Recently, a pioneering work, ProFL, explored the idea of unified debugging to unify fault localization and program repair in the other direction for the first time to boost both areas. More specifically, ProFL utilizes the patch execution results from one state-of-the-art repair system, PraPR, to help improve state-of-the-art fault localization. In this way, ProFL not only improves fault localization for manual repair, but also extends the application scope of automated repair to all possible bugs (not only the small ratio of bugs that can be automatically fixed). However, ProFL only considers one APR system (i.e., PraPR), and it is not clear how other existing APR systems based on different designs contribute to unified debugging. In this work, we perform an extensive study of the unified-debugging approach on 16 state-of-the-art program repair systems for the first time. Our experimental results on the widely studied Defects4J benchmark suite reveal various practical guidelines for unified debugging, such as (1) nearly all the studied 16 repair systems can positively contribute to unified debugging despite their varying repairing capabilities, (2) repair systems targeting multi-edit patches can bring extraneous noise into unified debugging, (3) repair systems with more executed/plausible patches tend to perform better for unified debugging, and (4) unified debugging effectiveness does not rely on the availability of correct patches in automated repair. Based on our results, we further propose an advanced unified debugging technique, UniDebug++, which can localize over 20% more bugs within Top-1 positions than state-of-the-art unified debugging technique, ProFL. 

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