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1. An Exploratory Study of Programmers' Analogical Reasoning and Software History Usage During Code Re-Purposing

   https://doi.org/10.1145/3641822.3641864  

   Allen, John; Kelleher, Caitlin (April 2024, ACM)

   Background: Software development relies on collaborative problem-solving. Understanding previously addressed problems in software is crucial for developers to identify and repurpose functionalities for new problem-solving contexts. Objective: We explore the barriers programmers encounter during code repurposing and investigate how access to historical context about the original developer's goals may affect this process. Method: We present an exploratory study of 16 programmers who completed two code repurposing tasks in different code bases. Participants completed these tasks both with and without access to the historical information of the original developer's goals. We explore how programmers use analogical reasoning to identify and apply existing software artifacts to new goals. Results: We show that programmers often failed to notice analogies, made false analogies, and underestimated the value of reuse. Even when useful analogies were made, programmers struggled to find the relevant code. We also describe the patterns of how participants utilized code histories. Conclusion: We highlight the barriers programmers face in noticing and applying analogies during code reuse. We suggest design recommendations for future tools to allow lightweight evaluation of code to help programmers identify reuse opportunities. 

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2. Visual analogy: Deep learning versus compositional models

   Ichien, N.; Liu, Q.; Fu, S.; Holyoak, K. J.; Yuille, A. L.; Lu, H. (July 2021, Proceedings of the 43rd Annual Meeting of the Cognitive Science Society)

   Fitch, T.; Lamm, C.; Leder, H.; Teßmar-Raible, K. (Ed.)

   Is analogical reasoning a task that must be learned to solve from scratch by applying deep learning models to massive numbers of reasoning problems? Or are analogies solved by computing similarities between structured representations of analogs? We address this question by comparing human performance on visual analogies created using images of familiar three-dimensional objects (cars and their subregions) with the performance of alternative computational models. Human reasoners achieved above-chance accuracy for all problem types, but made more errors in several conditions (e.g., when relevant subregions were occluded). We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) directly trained to solve these analogy problems, as well as to that of a compositional model that assesses relational similarity between part-based representations. The compositional model based on part representations, but not the deep learning models, generated qualitative performance similar to that of human reasoners. 

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3. Linking metaphor comprehension with analogical reasoning: Evidence from typical development and autism spectrum disorder

   https://doi.org/10.1111/bjop.12542  

   Morsanyi, Kinga; Hamilton, Jayne; Stamenković, Dušan; Holyoak, Keith J. (December 2021, British Journal of Psychology)

   Abstract We examined the relationship between metaphor comprehension and verbal analogical reasoning in young adults who were either typically developing (TD) or diagnosed with Autism Spectrum Disorder (ASD). The ASD sample was highly educated and high in verbal ability, and closely matched to a subset of TD participants on age, gender, educational background, and verbal ability. Additional TD participants with a broader range of abilities were also tested. Each participant solved sets of verbal analogies and metaphors in verification formats, allowing measurement of both accuracy and reaction times. Measures of individual differences in vocabulary, verbal working memory, and autistic traits were also obtained. Accuracy for both the verbal analogy and the metaphor task was very similar across the ASD and matched TD groups. However, reaction times on both tasks were longer for the ASD group. Additionally, stronger correlations between verbal analogical reasoning and working memory capacity in the ASD group indicated that processing verbal analogies was more effortful for them. In the case of both groups, accuracy on the metaphor and analogy tasks was correlated. A mediation analysis revealed that after controlling for working memory capacity, the inter‐task correlation could be accounted for by the mediating variable of vocabulary knowledge, suggesting that the primary common mechanisms linking the two tasks involve language skills. 

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4. Position: IntelliBlox: A Toolkit for Integrating Block-Based Programming into Game-Based Learning Environments

   https://doi.org/10.1109/BB48857.2019.8941222  

   Taylor, Sandra; Min, Wookhee; Mott, Bradford; Emerson, Andrew; Smith, Andy; Wiebe, Eric; Lester, James (October 2019, 2019 IEEE Blocks and Beyond Workshop (B&B))

   Block-based programming languages reduce the need to learn low-level programming syntax while enabling novice learners to focus on computational thinking skills. Game-based learning environments have been shown to create effective and engaging learning experiences for students in a broad range of educational domains. The fusion of block-based programming with game-based learning offers significant potential to motivate learners to develop computational thinking skills. A key challenge educational game developers face in creating rich, interactive learning experiences that integrate computational thinking activities is the lack of an embeddable block-based programming toolkit. Current block-based programming languages, such as Blockly and Scratch, cannot be easily embedded into industry-standard 3D game engines. This paper presents IntelliBlox, a Blockly-inspired toolkit for the Unity cross-platform game engine that enables learners to create block-based programs within immersive game-based learning environments. Our experience using IntelliBlox suggests that it is an effective toolkit for integrating block-based programming challenges into game-based learning environments. 

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5. Not quite any way you slice it: How different analogical constructions affect Raven's Matrices performance

   Yang, Y.; McGreggor, K.; Kunda, M. (January 2020, Proceedings of the Eighth Annual Conference on Advances in Cognitive Systems (ACS))

   null (Ed.)

   Analogical reasoning fundamentally involves exploiting redundancy in a given task, but there are many different ways an intelligent agent can choose to define and exploit redundancy, often resulting in very different levels of task performance. We explore such variations in analogical reasoning within the domain of geometric matrix reasoning tasks, namely on the Raven’s Standard Progressive Matrices intelligence test. We show how different analogical constructions used by the same basic visual-imagery-based computational model—varying only in how they “slice” a matrix problem into parts and do search and optimization within/across these parts—achieve very different levels of test performance, ranging from 13/60 correct all the way up to 57/60 correct. Our findings suggest that the ability to select or build effective high-level analogical constructions can be as important as an agent’s competencies in low-level reasoning skills, which raises interesting open questions about the extent to which building the “right” analogies might contribute to individual differences in human matrix reasoning performance, and how intelligent agents might learn to build or select from among different analogical constructions in the first place. 

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