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1. Making it Smart: Converting Static Code into an Interactive Trace Table

   Risha, Zak; Brusilovsky, Peter (August 2020, Sixth SPLICE Workshop "Building an Infrastructure for Computer Science Education Research and Practice at Scale" at ACM Learning at Scale 2020)

   This paper introduces a new type of smart learning content, an automatically generated trace table, that can easily integrate and adapt to existing curriculum and learning systems for computer science education. In addition to current features of the software, we describe how this tool constructs trace tables using only source code as an input. The potential of this tool is also explored by examining future opportunities in adaptation, feedback, and learning specifications. Last, we report a pilot integration into an existing system to demonstrate interoperability with a tangible use case. 

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2. Tool-Aided Assessment of Difficulties in Learning Formal Design-by-Contract Assertions

   https://doi.org/10.1145/3396802.3396807  

   Fowler, Megan; Kraemer, Eileen T.; Sun, Yu-Shan; Sitaraman, Murali; Hallstrom, Jason O.; Hollingsworth, Joseph E. (June 2020, ECSEE '20: Proceedings of the 4th European Conference on Software Engineering Education)

   Object-based development using design-by-contract (DbC) is broadly taught and practiced. Students must be able to read and write symbolic DbC assertions that are sufficiently precise and be able to use these assertions to trace program code. This paper summarizes the results of using an automated tool to pinpoint fine-grain difficulties students face in learning to symbolically trace code involving objects. The pilots were conducted in an undergraduate software engineering course. Quantitative results show that data collected by the tool can help to identify and classify learning obstacles. Qualitative findings help validate student misunderstandings underlying these difficulties. Analysis of exam questions helps understand the persistence of student learning to read and write simple assertions about code behavior. Together, these results provide directions for intervention. 

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3. Towards Semantically Guided Traceability

   Liu, Yalin; Lin, Jinfeng; Zeng, Qingkai; Jiang, Meng; Cleland-Huang, Jane (August 2020, International Conference on Requirements Engineering)

   In many regulated domains, traceability is established across diverse artifacts such as requirements, design, code, test cases, and hazards -- either manually or with the help of supporting tools, and the resulting trace links are used to support activities such as impact analysis, compliance verification, and safety inspections. Automated tracing techniques need to leverage the semantics of underlying artifacts in order to establish more accurate trace links and to provide explanations of links that have been created in either a manual or automated fashion. To support this, we propose an automated technique which leverages source code, project artifacts and an external domain corpus to generate a domain-specific concept model. We then use the generated concept model to improve traceability results and to provide explanations of the results. Our approach overcomes existing problems with deep-learning traceability algorithms, as it does not require a training set of existing trace links. Finally, as an initial proof-of-concept, we apply our semantically-guided approach to the Dronology project, and show that it improves over other tracing techniques that do not use a concept model. 

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4. Estimation of Differential Graphs via Log-Sum Penalized D-Trace Loss

   https://doi.org/10.1109/SSP53291.2023.10208014  

   Tugnait, Jitendra K. (July 2023, 2023 IEEE Statistical Signal Processing Workshop (SSP))

   We consider the problem of estimating differences in two Gaussian graphical models (GGMs) which are known to have similar structure. The GGM structure is encoded in its precision (inverse covariance) matrix. In many applications one is interested in estimating the difference in two precision matrices to characterize underlying changes in conditional dependencies of two sets of data. Most existing methods for differential graph estimation are based on a lasso penalized loss function. In this paper, we analyze a log-sum penalized D-trace loss function approach for differential graph learning. An alternating direction method of multipliers (ADMM) algorithm is presented to optimize the objective function. Theoretical analysis establishing consistency in estimation in high-dimensional settings is provided. We illustrate our approach using a numerical example where log-sum penalized D-trace loss significantly outperforms lasso-penalized D-trace loss as well as smoothly clipped absolute deviation (SCAD) penalized D-trace loss. 

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5. Joint Capped Norms Minimization for Robust Matrix Recovery

   https://doi.org/10.24963/ijcai.2017/356  

   Nie, Feiping; Huo, Zhouyuan; Huang, Heng (August 2017, The 26th International Joint Conference on Artificial Intelligence (IJCAI 2017))

   The low-rank matrix recovery is an important machine learning research topic with various scientific applications. Most existing low-rank matrix recovery methods relax the rank minimization problem via the trace norm minimization. However, such a relaxation makes the solution seriously deviate from the original one. Meanwhile, most matrix recovery methods minimize the squared prediction errors on the observed entries, which is sensitive to outliers. In this paper, we propose a new robust matrix recovery model to address the above two challenges. The joint capped trace norm and capped $$\ell_1$$-norm are used to tightly approximate the rank minimization and enhance the robustness to outliers. The evaluation experiments are performed on both synthetic data and real world applications in collaborative filtering and social network link prediction. All empirical results show our new method outperforms the existing matrix recovery methods. 

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