BiANE: Bipartite Attributed Network Embedding

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LLM for Test Script Generation and Migration: Challenges, Capabilities, and Opportunities

This paper investigates the application of large language models (LLM) in the domain of mobile application test script generation. Test script generation is a vital component of software testing, enabling efficient and reliable automation of repetitive test tasks. However, existing generation approaches often encounter limitations, such as difficulties in accurately capturing and reproducing test scripts across diverse devices, platforms, and applications. These challenges arise due to differences in screen sizes, input modalities, platform behaviors, API inconsistencies, and application architectures. Overcoming these limitations is crucial for achieving robust and comprehensive test automation. By leveraging the capabilities of LLMs, we aim to address these challenges and explore its potential as a versatile tool for test automation. We investigate how well LLMs can adapt to diverse devices and systems while accurately capturing and generating test scripts. Additionally, we evaluate its cross-platform generation capabilities by assessing its ability to handle operating system variations and platform-specific behaviors. Furthermore, we explore the application of LLMs in cross-app migration, where it generates test scripts across different applications and software environments based on existing scripts. Throughout the investigation, we analyze its adaptability to various user interfaces, app architectures, and interaction patterns, ensuring accurate script generation and compatibility. The findings of this research contribute to the understanding of LLMs' capabilities in test automation. Ultimately, this research aims to enhance software testing practices, empowering app developers to achieve higher levels of software quality and development efficiency.Comment: Accepted by the 23rd IEEE International Conference on Software Quality, Reliability, and Security (QRS 2023

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM

Backdooring Neural Code Search

Reusing off-the-shelf code snippets from online repositories is a common practice, which significantly enhances the productivity of software developers. To find desired code snippets, developers resort to code search engines through natural language queries. Neural code search models are hence behind many such engines. These models are based on deep learning and gain substantial attention due to their impressive performance. However, the security aspect of these models is rarely studied. Particularly, an adversary can inject a backdoor in neural code search models, which return buggy or even vulnerable code with security/privacy issues. This may impact the downstream software (e.g., stock trading systems and autonomous driving) and cause financial loss and/or life-threatening incidents. In this paper, we demonstrate such attacks are feasible and can be quite stealthy. By simply modifying one variable/function name, the attacker can make buggy/vulnerable code rank in the top 11%. Our attack BADCODE features a special trigger generation and injection procedure, making the attack more effective and stealthy. The evaluation is conducted on two neural code search models and the results show our attack outperforms baselines by 60%. Our user study demonstrates that our attack is more stealthy than the baseline by two times based on the F1 score

Universal Trading for Order Execution with Oracle Policy Distillation

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Towards effective execution strategy, recent years have witnessed the shift from the analytical view with model-based market assumptions to model-free perspective, i.e., reinforcement learning, due to its nature of sequential decision optimization. However, the noisy and yet imperfect market information that can be leveraged by the policy has made it quite challenging to build up sample efficient reinforcement learning methods to achieve effective order execution. In this paper, we propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. Particularly, this framework leverages a policy distillation method that can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information to approximate the optimal trading strategy. The extensive experiments have shown significant improvements of our method over various strong baselines, with reasonable trading actions.Comment: Accepted in AAAI 2021, the code and the supplementary materials are in https://seqml.github.io/opd

Globalization and Diversification of Interior Decoration Styles and Their Impact on Pakistani Handicrafts

With the gradual advancement of globalization, cultural values and traditional decorative practices as well as people’s lifestyles have changed through digital awareness. This has seriously affected the interior decoration industry and brought challenges to the local handicraft industry. Pakistan’s traditional handicraft is famous in the world, but the flow of imported machine-made products in Pakistan has led to a decline of Pakistan’s handicraft industry. This research investigated which factors are involved in home decoration and to see if these factors are advantageous for the handicraft industry or not. The use of handicraft decoration objects varies among people when they design their houses. Thus, study conducted a survey, which found several factors that Pakistani people emphasize in relation to home decoration. Currently, these factors do not benefit the handicraft industry as people pursue a modern lifestyle and they consider handicraft objects without innovation and design outdated. Interior decoration is seen as a major aspect of traditional Pakistani life and culture, with distinct cultural characteristics. Inspired by the latest way of thinking, an interior design first exists as a concept and is then realized through careful planning. It is meant to evoke a specific mood through the strategic use of color, space, and style. In Pakistan, home accessories are generally and strongly influenced by the current fashion in the end market, with many changes brought about by consumers’ purchasing patterns, designers’ and decorators’ styles, and economic conditions. Keeping up with constantly changing trends is the main challenge faced by the traditional handicraft industry sector. In many cases, craftspeople are disconnected from the end market, which is a challenge for them to be able to profit from their work

Globalization and Diversification of Interior Decoration Styles and Their Impact on Pakistani Handicrafts

With the gradual advancement of globalization, cultural values and traditional decorative practices as well as people’s lifestyles have changed through digital awareness. This has seriously affected the interior decoration industry and brought challenges to the local handicraft industry. Pakistan’s traditional handicraft is famous in the world, but the flow of imported machine-made products in Pakistan has led to a decline of Pakistan’s handicraft industry. This research investigated which factors are involved in home decoration and to see if these factors are advantageous for the handicraft industry or not. The use of handicraft decoration objects varies among people when they design their houses. Thus, study conducted a survey, which found several factors that Pakistani people emphasize in relation to home decoration. Currently, these factors do not benefit the handicraft industry as people pursue a modern lifestyle and they consider handicraft objects without innovation and design outdated. Interior decoration is seen as a major aspect of traditional Pakistani life and culture, with distinct cultural characteristics. Inspired by the latest way of thinking, an interior design first exists as a concept and is then realized through careful planning. It is meant to evoke a specific mood through the strategic use of color, space, and style. In Pakistan, home accessories are generally and strongly influenced by the current fashion in the end market, with many changes brought about by consumers’ purchasing patterns, designers’ and decorators’ styles, and economic conditions. Keeping up with constantly changing trends is the main challenge faced by the traditional handicraft industry sector. In many cases, craftspeople are disconnected from the end market, which is a challenge for them to be able to profit from their work