This qualitative research performs a thematic analysis of the learning objectives in existing project-based undergraduate softwareengineering courses to align them with the competency model defined in the Computing C...
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A crucial activity in software maintenance and evolution is the comprehension of the changes performed by developers, when they submit a pull request and/or perform a commit on the repository. Typically, code changes ...
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ISBN:
(纸本)9798350395693;9798350395686
A crucial activity in software maintenance and evolution is the comprehension of the changes performed by developers, when they submit a pull request and/or perform a commit on the repository. Typically, code changes are represented in the form of code diffs, textual representations highlighting the differences between two file versions, depicting the added, removed, and changed lines. This simplistic representation must be interpreted by developers, and mentally lifted to a higher abstraction level, that more closely resembles natural language descriptions, and eases the creation of a mental model of the changes. However, the textual diff-based representation is cumbersome, and the lifting requires considerable domain knowledge and programming skills. We present an approach, based on the concept of micro-change, to overcome these difficulties, translating code diffs into a series of pre-defined change operations, which can be described in natural language. We present a catalog of micro-changes, together with an automated micro-change detector. To evaluate our approach, we performed an empirical study on a large set of open-source repositories, focusing on a subset of our micro-change catalog, namely those related to changes affecting the conditional logic. We found that our detector is capable of explaining more than 67% of the changes taking place in the systems under study.
This paper presents a simulator generator designed to aid in the development of domain-specific protocols that enable semantic communication, where messages include annotations specifying the meaning of individual fie...
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This work combines structural mechanical prior knowledge with cooperative Bayesian optimization framework and proposes a domain knowledge-based multilevel grouping approach for mid-ship section optimization. Cooperati...
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software documentation captures detailed knowledge about a software product, e.g., code, technologies, and design. It plays an important role in the coordination of development teams and in conveying ideas to various ...
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ISBN:
(纸本)9798400703270
software documentation captures detailed knowledge about a software product, e.g., code, technologies, and design. It plays an important role in the coordination of development teams and in conveying ideas to various stakeholders. However, software documentation can be hard to comprehend if it is written with jargon and complicated sentence structure. In this study, we explored the potential of text simplification techniques in the domain of softwareengineering to automatically simplify GitHub README files. We collected software-related pairs of GitHub README files consisting of 14,588 entries, aligned difficult sentences with their simplified counterparts, and trained a Transformer-based model to automatically simplify difficult versions. To mitigate the sparse and noisy nature of the software-related simplification dataset, we applied general text simplification knowledge to this field. Since many general-domain difficult-to-simple Wikipedia document pairs are already publicly available, we explored the potential of transfer learning by first training the model on the Wikipedia data and then fine-tuning it on the README data. Using automated BLEU scores and human evaluation, we compared the performance of different transfer learning schemes and the baseline models without transfer learning. The transfer learning model using the best checkpoint trained on a general topic corpus achieved the best performance of 34.68 BLEU score and statistically significantly higher human annotation scores compared to the rest of the schemes and baselines. We conclude that using transfer learning is a promising direction to circumvent the lack of data and drift style problem in software README files simplification and achieved a better trade-off between simplification and preservation of meaning.
Although the current ERP (Enterprise Resource Planning) course has the latest information system software as in the industry that can be operated in class, and with the refinement of PBL and teaching methods, students...
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ISBN:
(纸本)9783031519789;9783031519796
Although the current ERP (Enterprise Resource Planning) course has the latest information system software as in the industry that can be operated in class, and with the refinement of PBL and teaching methods, students can learn from the perspective of system implementation, rather than the traditional system operation level. However, for most students, they are still more inclined to work data input, and cannot connect to process-oriented knowledge, or even processes analysis, causing students to fall into repetitive and monotonous system operations. Therefore, this study intends to apply the digital online game competition in the ERP courses of the technical University students. To evaluate the "game-based teaching plan and course design strategy", a statistical comparative analysis is carried out on the impact of the ERP course plan. It is hoped that through such an instructional design integrating PBL and game-based learning, it can encourage students' spontaneous and interesting learning motivation and improve the effect of learning participation.
作者:
Su, LishaYan, RongCollege of Computer Science
Inner Mongolia University Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China
Joint extraction of entities and relations is an essential task in information extraction. Recently, tagging-based models have gained attention but with poor performance on overlapping triplets, which confronted the i...
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The active participation of contributors is key to the success of any open source software (OSS) projects. In the open source community, it can be found that some developers have participated together in multiple proj...
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Access to new technologies in the educational process and developing digital competences enabling critical and responsible use of digital technologies, including future professional work is a challenge for universitie...
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This experience paper describes thirteen considerations for implementing machine learning software defect prediction (ML SDP) in vivo. Specifically, we provide the following report on the ground of the most important ...
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ISBN:
(纸本)9798350329964
This experience paper describes thirteen considerations for implementing machine learning software defect prediction (ML SDP) in vivo. Specifically, we provide the following report on the ground of the most important observations and lessons learned gathered during a large-scale research effort and introduction of ML SDP to the system-level testing quality assurance process of one of the leading telecommunication vendors in the world - Nokia. We adhere to a holistic and logical progression based on the principles of the business analysis body of knowledge: from identifying the need and setting requirements, through designing and implementing the solution, to profitability analysis, stakeholder management, and handover. Conversely, for many years, industry adoption has not kept up the pace of academic achievements in the field, despite promising potential to improve quality and decrease the cost of software products for many companies worldwide. Therefore, discussed considerations hopefully help researchers and practitioners bridge the gaps between academia and industry.
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