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Component-based software development (CBD) is a methodology that has been embraced by the software industry to accelerate development, save costs and timelines, minimize testing requirements, and boost quality and output. Compared to the conventional software development approach, this led to the system's development being completed more quickly. By choosing components, identifying systems, and evaluating those systems, CBSE contributes significantly to the software development process. The objective of CBSE is to codify and standardize all disciplines that support CBD-related operations. Analysis of the comparison between component-based and scripting technologies reveals that, in terms of qualitative performance, component-based technologies scale more effectively. Further study and application of CBSE are directly related to the CBD approach's success. This paper explores the introductory concepts and comparative analysis related to component-based software engineering which have been around for a while, but proper adaption of CBSE are still lacking issues are also focused.

Wake word detection exists in most intelligent homes and portable devices. It offers these devices the ability to "wake up" when summoned at a low cost of power and computing. This paper focuses on understanding alignment's role in developing a wake-word system that answers a generic phrase. We discuss three approaches. The first is alignment-based, where the model is trained with frame-wise cross-entropy. The second is alignment-free, where the model is trained with CTC. The third, proposed by us, is a hybrid solution in which the model is trained with a small set of aligned data and then tuned with a sizeable unaligned dataset. We compare the three approaches and evaluate the impact of the different aligned-to-unaligned ratios for hybrid training. Our results show that the alignment-free system performs better than the alignment-based for the target operating point, and with a small fraction of the data (20%), we can train a model that complies with our initial constraints.

To support software developers in understanding and maintaining programs, various automatic code summarization techniques have been proposed to generate a concise natural language comment for a given code snippet. Recently, the emergence of large language models (LLMs) has led to a great boost in the performance of natural language processing tasks. Among them, ChatGPT is the most popular one which has attracted wide attention from the software engineering community. However, it still remains unclear how ChatGPT performs in (automatic) code summarization. Therefore, in this paper, we focus on evaluating ChatGPT on a widely-used Python dataset called CSN-Python and comparing it with several state-of-the-art (SOTA) code summarization models. Specifically, we first explore an appropriate prompt to guide ChatGPT to generate in-distribution comments. Then, we use such a prompt to ask ChatGPT to generate comments for all code snippets in the CSN-Python test set. We adopt three widely-used metrics (including BLEU, METEOR, and ROUGE-L) to measure the quality of the comments generated by ChatGPT and SOTA models (including NCS, CodeBERT, and CodeT5). The experimental results show that in terms of BLEU and ROUGE-L, ChatGPT's code summarization performance is significantly worse than all three SOTA models. We also present some cases and discuss the advantages and disadvantages of ChatGPT in code summarization. Based on the findings, we outline several open challenges and opportunities in ChatGPT-based code summarization.

ChatGPT demonstrates immense potential to transform software engineering (SE) by exhibiting outstanding performance in tasks such as code and document generation. However, the high reliability and risk control requirements of SE make the lack of interpretability for ChatGPT a concern. To address this issue, we carried out a study evaluating ChatGPT's capabilities and limitations in SE. We broke down the abilities needed for AI models to tackle SE tasks into three categories: 1) syntax understanding, 2) static behavior understanding, and 3) dynamic behavior understanding. Our investigation focused on ChatGPT's ability to comprehend code syntax and semantic structures, including abstract syntax trees (AST), control flow graphs (CFG), and call graphs (CG). We assessed ChatGPT's performance on cross-language tasks involving C, Java, Python, and Solidity. Our findings revealed that while ChatGPT excels at understanding code syntax (AST), it struggles with comprehending code semantics, particularly dynamic semantics. We conclude that ChatGPT possesses capabilities akin to an Abstract Syntax Tree (AST) parser, demonstrating initial competencies in static code analysis. Additionally, our study highlights that ChatGPT is susceptible to hallucination when interpreting code semantic structures and fabricating non-existent facts. These results underscore the need to explore methods for verifying the correctness of ChatGPT's outputs to ensure its dependability in SE. More importantly, our study provide an iniital answer why the generated codes from LLMs are usually synatx correct but vulnerabale.

Background: Previous research highlights that common misconceptions about developer productivity lead to harmful and inaccurate evaluations of software work, pointing to the need for organizations to differentiate between measures of production, productivity, and performance as an important step that helps to suggest improvements to how we measure the success of engineering teams. Methodology: Using a card sort, we explored how a Three Layer Productivity Framework was used by 16 software engineers at a Software Engineering focused conference to rank measures of success, first in the current practice of their organization and second in their individual beliefs about the best ways to measure engineering success. Results and discussion: Overall, participants preferred organizations to 1) continue their prioritized focus on performance layer metrics, 2) increase the focus on productivity metrics, and 3) decrease their focus on production metrics. When asked about the current metrics of their organizations, while all roles reported a current focus on performance metrics, only ICs reported a strong focus on production metrics. When asked about metrics they would prefer, all roles preferred more performance metrics but only leaders and ICs also wanted productivity metrics. While all participants were aligned on performance metrics being a top preference, there was misalignment on which specific metrics are used. Our findings show that when measuring developer success, organizations should continue measurement using performance metrics, consider an increased focus on productivity metrics, and consider a decreased focus on production metrics.

We propose a method for unsupervised opinion summarization that encodes sentences from customer reviews into a hierarchical discrete latent space, then identifies common opinions based on the frequency of their encodings. We are able to generate both abstractive summaries by decoding these frequent encodings, and extractive summaries by selecting the sentences assigned to the same frequent encodings. Our method is attributable, because the model identifies sentences used to generate the summary as part of the summarization process. It scales easily to many hundreds of input reviews, because aggregation is performed in the latent space rather than over long sequences of tokens. We also demonstrate that our appraoch enables a degree of control, generating aspect-specific summaries by restricting the model to parts of the encoding space that correspond to desired aspects (e.g., location or food). Automatic and human evaluation on two datasets from different domains demonstrates that our method generates summaries that are more informative than prior work and better grounded in the input reviews.

Visually impaired (VI) people often face challenges when performing everyday tasks and identify shopping for clothes as one of the most challenging. Many engage in online shopping, which eliminates some challenges of physical shopping. However, clothes shopping online suffers from many other limitations and barriers. More research is needed to address these challenges, and extant works often base their findings on interviews alone, providing only subjective, recall-biased information. We conducted two complementary studies using both observational and interview approaches to fill a gap in understanding about VI people's behaviour when selecting and purchasing clothes online. Our findings show that shopping websites suffer from inaccurate, misleading, and contradictory clothing descriptions; that VI people mainly rely on (unreliable) search tools and check product descriptions by reviewing customer comments. Our findings also indicate that VI people are hesitant to accept assistance from automated, but that trust in such systems could be improved if researchers can develop systems that better accommodate users' needs and preferences.

[Context] Artificial intelligence (AI) components used in building software solutions have substantially increased in recent years. However, many of these solutions focus on technical aspects and ignore critical human-centered aspects. [Objective] Including human-centered aspects during requirements engineering (RE) when building AI-based software can help achieve more responsible, unbiased, and inclusive AI-based software solutions. [Method] In this paper, we present a new framework developed based on human-centered AI guidelines and a user survey to aid in collecting requirements for human-centered AI-based software. We provide a catalog to elicit these requirements and a conceptual model to present them visually. [Results] The framework is applied to a case study to elicit and model requirements for enhancing the quality of 360 degree~videos intended for virtual reality (VR) users. [Conclusion] We found that our proposed approach helped the project team fully understand the human-centered needs of the project to deliver. Furthermore, the framework helped to understand what requirements need to be captured at the initial stages against later stages in the engineering process of AI-based software.

Classic machine learning methods are built on the $i.i.d.$ assumption that training and testing data are independent and identically distributed. However, in real scenarios, the $i.i.d.$ assumption can hardly be satisfied, rendering the sharp drop of classic machine learning algorithms' performances under distributional shifts, which indicates the significance of investigating the Out-of-Distribution generalization problem. Out-of-Distribution (OOD) generalization problem addresses the challenging setting where the testing distribution is unknown and different from the training. This paper serves as the first effort to systematically and comprehensively discuss the OOD generalization problem, from the definition, methodology, evaluation to the implications and future directions. Firstly, we provide the formal definition of the OOD generalization problem. Secondly, existing methods are categorized into three parts based on their positions in the whole learning pipeline, namely unsupervised representation learning, supervised model learning and optimization, and typical methods for each category are discussed in detail. We then demonstrate the theoretical connections of different categories, and introduce the commonly used datasets and evaluation metrics. Finally, we summarize the whole literature and raise some future directions for OOD generalization problem. The summary of OOD generalization methods reviewed in this survey can be found at //out-of-distribution-generalization.com.

We introduce a generic framework that reduces the computational cost of object detection while retaining accuracy for scenarios where objects with varied sizes appear in high resolution images. Detection progresses in a coarse-to-fine manner, first on a down-sampled version of the image and then on a sequence of higher resolution regions identified as likely to improve the detection accuracy. Built upon reinforcement learning, our approach consists of a model (R-net) that uses coarse detection results to predict the potential accuracy gain for analyzing a region at a higher resolution and another model (Q-net) that sequentially selects regions to zoom in. Experiments on the Caltech Pedestrians dataset show that our approach reduces the number of processed pixels by over 50% without a drop in detection accuracy. The merits of our approach become more significant on a high resolution test set collected from YFCC100M dataset, where our approach maintains high detection performance while reducing the number of processed pixels by about 70% and the detection time by over 50%.