Programming languages can benefit from one another by utilizing a pre-trained model for software engineering tasks such as code summarization and method name prediction. While full fine-tuning of Code Language Models (Code-LMs) has been explored for multilingual knowledge transfer, research on Parameter Efficient Fine-Tuning (PEFT) for this purpose is lim-ited. AdapterFusion, a PEFT architecture, aims to enhance task performance by leveraging information from multiple languages but primarily focuses on the target language. To address this, we propose AdvFusion, a novel PEFT-based approach that effectively learns from other languages before adapting to the target task. Evaluated on code summarization and method name prediction, AdvFusion outperforms AdapterFusion by up to 1.7 points and surpasses LoRA with gains of 1.99, 1.26, and 2.16 for Ruby, JavaScript, and Go, respectively. We open-source our scripts for replication purposes11https://github.com/ist1373/AdvFusion.

Pre-trained Programming Language Models (PPLMs) achieved many recent states of the art results for many code-related software engineering tasks. Though some studies use data flow or propose tree-based models that utilize Abstract Syntax Tree (AST), most PPLMs do not fully utilize the rich syntactical information in source code. Still, the input is considered a sequence of tokens. There are two issues; the first is computational inefficiency due to the quadratic relationship between input length and attention complexity. Second, any syntactical information, when needed as an extra input to the current PPLMs, requires the model to be pre-trained from scratch, wasting all the computational resources already used for pre-training the current models. In this work, we propose Named Entity Recognition (NER) adapters, lightweight modules that can be inserted into Transformer blocks to learn type information extracted from the AST. These adapters can be used with current PPLMs such as CodeBERT, GraphCodeBERT, and CodeT5. We train the NER adapters using a novel Token Type Classification objective function (TTC). We insert our proposed work in CodeBERT, building CodeBERTER, and evaluate the performance on two tasks of code refinement and code summarization. CodeBERTER improves the accuracy of code refinement from 16.4 to 17.8 while using 20% of training parameter budget compared to the fully fine-tuning approach, and the BLEU score of code summarization from 14.75 to 15.90 while reducing 77% of training parameters compared to the fully fine-tuning approach.

Specification synthesis is the process of deriving a model from the input-output traces of a system. It is used extensively in test design, reverse engineering, and system identification. One type of the resulting artifact of this process for cyber-physical systems is hybrid automata. They are intuitive, precise, tool independent, and at a high level of abstraction, and can model systems with both discrete and continuous variables. In this article, we propose a new technique for synthesizing hybrid automaton from the input-output traces of a non-linear cyber-physical system. Similarity detection in non-linear behaviors is the main challenge for extracting such models. We address this problem by utilizing the Dynamic Time Warping technique. Our approach is passive, meaning that it does not need interaction with the system during automata synthesis from the logged traces; and online, which means that each input/output trace is used only once in the procedure. In other words, each new trace can be used to improve the already synthesized automaton. We evaluated our algorithm in one industrial and two simulated case studies. The accuracy of the derived automata shows promising results.

The ISO 27001 is the most adopted international information security management standard, by several countries and industries. This paper looks closely to the impacts of cultural characteristics on different phases of developing ISO 27001, based on three levels (country, organisational, and personal), which is especially helpful for Small and Medium Enterprises (SMEs). Cultural dimensions can significantly affect organisational administration and achievements such as decision-making, innovation and new practices, work motivation, negotiation, human resource practices, and leadership. The results are mainly based on a literature review, such as Hofstede and their relationship with the ISO 27001 Annex A. The outcomes of this paper illustrate that national (country level) cultural dimensions have high impact on the success and effectiveness of the ISO 27001 development phases.

The part of the management system of an organization dealing with information security is called Information Security Management System (ISMS). The most adopted ISMS standard is ISO 27001:2005. The 2005 version of the standard has been updated in 2013 to provide more clarity and more freedom in implementation, based on practical experiences. This paper compares ISO 27001:2005 and the updated 2013 standard, based on Annex A controls. We classify the controls into five categories of data, hardware, software, people and network. All of the controls defined in Annex A, regardless of their objectives, can easily be allocated to at least one of these categories. Classifying the controls to known categories offers an integrated view of the updated standard and presents a suitable guide for evaluating the performance and efficiency of the updated standard.