Foundation models (FMs), such as Large Language Models (LLMs), have revolutionized software development by enabling new use cases and business models. We refer to software built using FMs as FMware. The unique propert...
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software repositories have a plethora of information about software development, encompassing details such as code contributions, bug reports and code reviews. This rich source of data can be harnessed to enhance not ...
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ISBN:
(纸本)9798400717017
software repositories have a plethora of information about software development, encompassing details such as code contributions, bug reports and code reviews. This rich source of data can be harnessed to enhance not only software quality and development velocity but also to gain insights into team collaboration and inform strategic decision-making throughout the software development lifecycle. Previous studies show that many stakeholders cannot benefit from the project information due to the technical knowledge and expertise required to extract the project data. To lower the barrier to entry by automating the process of extracting and analyzing repository data, we explored the potential of using an LLM to develop a chatbot for answering questions related to software repositories. We evaluated the chatbot on 150 software repository-related questions. We found that the chatbot correctly answered one question. This result prompted us to shift our focus to investigate the challenges in adopting LLMs for the out-of-thebox development of software repository chatbots. We identified five main challenges related to retrieving data, structuring the data, and generating the answer to the user's query. Among these challenges, the most frequent (83.3%) is the inaccurate retrieval of data to answer questions. In this paper, we share our experience and challenges in developing an LLM-based chatbot to answer software repository-related questions within the SE community. We also provide recommendations on mitigating these challenges. Our findings will serve as a foundation to drive future research aimed at enhancing LLMs for adoption in extracting useful information from software repositories, fostering advancements in natural language understanding, data retrieval, and response generation within the context of software repository-related questions and analytics.
The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various ...
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Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used...
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ISBN:
(纸本)9798400704901
Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to guide the trials of different hyperparameter configurations amongst a specific search space. However, many trials may encounter severe training problems, such as vanishing gradients and insufficient convergence, which can hardly be reflected by accuracy metrics in the early stages of the training and often result in poor performance. This leads to an inefficient optimization trajectory because the bad trials occupy considerable computation resources and reduce the probability of finding excellent hyperparameter configurations within a time limitation. In this paper, we propose Bad Trial Tackler (BTTackler), a novel HPO framework that introduces training diagnosis to identify training problems automatically and hence tackles bad trials. BTTackler diagnoses each trial by calculating a set of carefully designed quantified indicators and triggers early termination if any training problems are detected. Evaluations are performed on representative HPO tasks consisting of three classical deep neural networks (DNN) and four widely used HPO methods. To better quantify the effectiveness of an automated HPO method, we propose two new measurements based on accuracy and time consumption. Results show the advantage of BTTackler on two-fold: (1) it reduces 40.33% of time consumption to achieve the same accuracy comparable to baseline methods on average and (2) it conducts 44.5% more top-10 trials than baseline methods on average within a given time budget. We also released an open-source Python library that allows users to easily apply BTTackler to automated HPO processes with minimal code changes.
Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders' perspectives, designers' intellect, tool-based automation, pattern-driven reuse, and...
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ISBN:
(纸本)9798400700446
Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders' perspectives, designers' intellect, tool-based automation, pattern-driven reuse, and so on, to sketch a blueprint that guides software implementation and evaluation. Despite its benefits, architecture-centric softwareengineering (ACSE) suffers from a multitude of challenges. ACSE challenges could stem from a lack of standardized processes, socio-technical limitations, and scarcity of human expertise etc. that can impede the development of existing and emergent classes of software. software Development Bots (DevBots) trained on large language models can help synergise architects' knowledge with artificially intelligent decision support to enable rapid architecting in a human-bot collaborative ACSE. An emerging solution to enable this collaboration is ChatGPT, a disruptive technology not primarily introduced for softwareengineering, but is capable of articulating and refining architectural artifacts based on natural language processing. We detail a case study that involves collaboration between a novice software architect and ChatGPT to architect a service-basedsoftware. Future research focuses on harnessing empirical evidence about architects' productivity and explores socio-technical aspects of architecting with ChatGPT to tackle challenges of ACSE.
The core of design lies in the acquisition and application of knowledge. knowledge push technology can effectively improve the utilization efficiency of knowledge by designers, thus enabling more efficient task comple...
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knowledge graph completion is to solve the problem of lack of entities and relations in knowledge graphs. Existing knowledge graph completion methods mainly embed entities and relations into latent vectors, and numero...
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ISBN:
(纸本)9798400708305
knowledge graph completion is to solve the problem of lack of entities and relations in knowledge graphs. Existing knowledge graph completion methods mainly embed entities and relations into latent vectors, and numerous researches have taken the rich relationships in knowledge graph into account such as category, entity, relation and semantic. However, only a few studies consider the relation between attributes, which is the basis of describing the entity. This paper proposes the Attribute Hierarchy knowledge Graph Completion (AH-KGC) method, aiming at leveraging the attribute relation to find the missing obligatory property of entities. Primarily, in AH-KGC, we have discussed the attributes prerequisite relation, which can be described as a tree-like hierarchical structure, and then adopt the search algorithm of preorder traversal based on the hierarchy to find out the missing attributes. Specifically, we prove that attribute prerequisite is a special case of implication, thus can obtain attribute hierarchy from implications relation, which can easily be obtained in much mature research such as expert systems and make full use of the knowledge in various fields to make up for the vacancy of domain knowledge in the knowledge graph. The experiment has been performed on the CN-DBpedia and Probase datasets. The result demonstrates that AH-KGC can effectively complete the missing attributes of entities in knowledge graphs and achieve 100% accuracy under our evaluation system.
Numerous software defect prediction methods utilize semantic information and software metrics as code features, neglecting the structural knowledge inherent in the source code. Other studies improve feature completene...
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Infrastructure-from-Code (IfC) is a new approach to DevOps and an advancement of Infrastructure-as-Code (IaC). One of its key concepts is to provide a higher level of abstraction facilitated by new programming languag...
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ISBN:
(纸本)9798400711800
Infrastructure-from-Code (IfC) is a new approach to DevOps and an advancement of Infrastructure-as-Code (IaC). One of its key concepts is to provide a higher level of abstraction facilitated by new programming languages or software development kits, which automatically generate the necessary code and configurations to provision the infrastructure, deploy the application, andmanage the cloud services. IfC approaches promise higher developer productivity by reducing DevOps-specific tasks and the expert knowledge required. However, empirical studies on developers' performance, perceived ease of use, and usability related to IfC are missing. We conducted a controlled experiment (n=40) to assess the usability of the cloud programming languages (PL) and software development kits (SDK). Both approaches involve similar effectiveness. We found that the PL-based approach was moderately less efficient but increased correctness with time spent on programming. Tracing generated infrastructure configurations from code was more challenging with the SDK-based approach. Applying thematic analysis, 19 themes emerged related to usability barriers, supporting factors, security, cloud cost, and enhancement areas. We conclude with five findings and future directions.
The proceedings contain 79 papers. The topics discussed include: an interactive approach for query-based multi-document scientific text summarization;enhancing Persian word sense disambiguation with large language mod...
ISBN:
(纸本)9798331511272
The proceedings contain 79 papers. The topics discussed include: an interactive approach for query-based multi-document scientific text summarization;enhancing Persian word sense disambiguation with large language models techniques and applications;assessing users' influence on respondents in conversation quality: a quantitative study on reddit based on the cooperative principle;non-negative matrix factorization improves residual neural networks;cluster sampling: a cluster-driven sampling strategy for deep metric learning;a scalable blockchain-based educational network for data storage and assessment;towards efficient capsule networks through approximate squash function and layer-wise quantization;automated software design using machine learning with natural language processing;evaluation of efficient electrocardiomatrix-based identification using deep learning methods;disturbance rejection in quadruple-tank system by proposing new method in reinforcement learning;and an improved and accurate measure for mining correlated high-utility itemsets.
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