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Title: CGenProg: Adaptation of cartesian genetic programming with migration and opposite guesses for automatic repair of software regression faults
Authors: Alireza Khalilian, Ahmad Baraani-Dastjerdi, Bahman Zamani
Journal: Expert Systems with Applications
Date: 1 May 2021
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Highlights

• CGenProg proposed for automatic repair of software regression faults in Java programs.
• Cartesian genetic programming as the core evolutionary algorithm was adapted and modified.
• Biogeography-based optimization (migration) as the crossover was adapted.
• Opposition-based learning (opposite guesses) as the mutation was adapted.

Title: Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI
Authors: Holzinger Andreas, Malle Bernd, Saranti Anna, Pfeifer Bastian. (2021)
Journal: Information Fusion
Publisher: Elsevier

The authors describe a novel, holistic approach to an automated medical decision pipeline, building on state-of-the-art Machine Learning research, yet integrating the human-in-the-loop via an innovative, interactive & exploration-based explainability technique called counterfactual graphs. They outline the necessity of computing a joint multi-modal representation space in a decentralized fashion, for the reasons of scalability and performance as well as ever-evolving data protection regulations. This effort is indented as a motivation for the international research community and a launchpad for further work in the fields of multi-modal embeddings, interactive explainability, counterfactuals, causability, as well as necessary foundations for effective future human–AI interfaces.

More: https://featurecloud.eu/wp-content/uploads/2021/03/Holzinger-et-al_2021_Towards-multi-model-causability.pdf

Title: Sustainable computational science: the ReScience initiative
Authors: Nicolas Rougier,  Hinsen Konrad and others
Journal: PeerJ Computer Science
Publisher: PeerJ Inc.

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results; however, computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true.
James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews.  Existing journals have been slow to adapt: source codes are rarely requested and are hardly ever actually executed to check that they produce the results advertised in the article.
ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.

More: https://www.labri.fr/perso/nrougier/papers/10.7717.peerj-cs.142.pdf

Researchers from Barcelona Supercomputing Center (Spain) and Abdullah Gul University in Kayseri (Turkey) are sharing an approach to apply real under-volting SRAM fault maps to a simulated system and observe the resiliency of the applications.
They compare the hardware guided fault injection approach with a random guided fault injection approach. Significant differences appears in the coarse categorization of the resiliency of the application, which become more obvious as the number of faulty bits increases. There are also differences when inspecting the quality of the output among the two techniques. This is because in an realisticsystem  not all fault locations have the same probability to  present faults, therefore from the software  perspective the faults can propagate to a limited number of software structures.

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An article signed by Idan Amit and Dror G. Feitelson from the Department of Computer Science at the Hebrew University of Jerusalem, presents a code quality metric, the Corrective Commit Probability (CCP).

This metric measures the probability that a commit reflects corrective maintenance. The authors think that this metric agrees with developers’ concept of quality, informative, and stable. Corrective commits are identified by applying a linguistic model to the commit messages. The  team compute the CCP of all large active GitHub projects (7,557 projects with 200+ com-mits in 2019). This leads to the creation of a quality scale, suggesting that the bottom 10% of quality projects spend at least 6 times more effort on fixing bugs than the top 10%. Analysis of project attributes shows that lower CCP (higher quality) is associated with smaller files, lower coupling, use of languages like JavaScript and C# as opposed to PHP and C++, fewer developers, lower developer churn, better on boarding, and  better  productivity. Among  other  things these results support the “Quality is Free” claim, and suggest that achieving higher quality need not require higher expenses.

• Read the full CCP paper