Packet classification is crucial for network security, traffic management, and quality of service by enabling efficient identification and handling of data packets. Decision tree-based rule partitioning has emerged as a prominent method in recent research. A significant challenge for decision tree algorithms is rule replication, which occurs when rules span multiple subspaces, leading to substantial memory consumption increases. Rule partitioning can effectively mitigate or eliminate this replication by separating overlapping rules. However, existing partitioning techniques heavily rely on manual parameter tuning across a wide range of possible values, making optimal solution discovery challenging. Furthermore, due to the lack of global optimization, these approaches face a critical trade-off: either the number of subsets becomes uncontrollable, resulting in diminished query speed, or rule replication becomes severe, causing substantial memory overhead. To bridge these gaps and achieve high-performance adaptive partitioning, we propose ERPC, a novel algorithm with the following key features: First, ERPC leverages graph theory to model rule sets, enabling global optimization that balances intra-group rule replication against the total number of groups. Second, ERPC advances rule set partitioning by modifying traditional community detection algorithms, strategically shifting the optimization objective from positive to negative modularity. Third, ERPC allows the rule set itself to determine the optimal number of groups, thus eliminating the need for manual parameter tuning. Experimental results demonstrate the efficacy of ERPC when applied to CutSplit, a state-of-the-art multi-tree method. It preserves 88% of CutSplit’s average classification throughput while reducing tree-building time by 89% and memory consumption by 77%. Furthermore, ERPC exhibits strong scalability, being adaptable to mainstream decision tree methods.

According to the World Health Organization(WHO), it is estimated that approximately 1.3 billion people live with some forms of vision impairment globally, of whom 36 million are blind. Due to their disability, engaging these minority into the society is a challenging problem. The recent rise of smart mobile phones provides a new solution by enabling blind users' convenient access to the information and service for understanding the world. Users with vision impairment can adopt the screen reader embedded in the mobile operating systems to read the content of each screen within the app, and use gestures to interact with the phone. However, the prerequisite of using screen readers is that developers have to add natural-language labels to the image-based components when they are developing the app. Unfortunately, more than 77% apps have issues of missing labels, according to our analysis of 10,408 Android apps. Most of these issues are caused by developers' lack of awareness and knowledge in considering the minority. And even if developers want to add the labels to UI components, they may not come up with concise and clear description as most of them are of no visual issues. To overcome these challenges, we develop a deep-learning based model, called LabelDroid, to automatically predict the labels of image-based buttons by learning from large-scale commercial apps in Google Play. The experimental results show that our model can make accurate predictions and the generated labels are of higher quality than that from real Android developers.

GUI animations, such as card movement, menu slide in/out, snackbar display, provide appealing user experience and enhance the usability of mobile applications. These GUI animations should not violate the platform's UI design guidelines (referred to as design-don't guideline in this work) regarding component motion and interaction, content appearing and disappearing, and elevation and shadow changes. However, none of existing static code analysis, functional GUI testing and GUI image comparison techniques can "see" the GUI animations on the scree, and thus they cannot support the linting of GUI animations against design-don't guidelines. In this work, we formulate this GUI animation linting problem as a multi-class screencast classification task, but we do not have sufficient labeled GUI animations to train the classifier. Instead, we propose an unsupervised, computer-vision based adversarial autoencoder to solve this linting problem. Our autoencoder learns to group similar GUI animations by "seeing" lots of unlabeled real-application GUI animations and learning to generate them. As the first work of its kind, we build the datasets of synthetic and real-world GUI animations. Through experiments on these datasets, we systematically investigate the learning capability of our model and its effectiveness and practicality for linting GUI animations, and identify the challenges in this linting problem for future work.

UI design is an integral part of software development. For many developers who do not have much UI design experience, exposing them to a large database of real-application UI designs can help them quickly build up a realistic understanding of the design space for a software feature and get design inspirations from existing applications. However, existing keyword-based, image-similarity-based, and component-matching-based methods cannot reliably find relevant high-fidelity UI designs in a large database alike to the UI wireframe that the developers sketch, in face of the great variations in UI designs. In this article, we propose a deep-learning-based UI design search engine to fill in the gap. The key innovation of our search engine is to train a wireframe image autoencoder using a large database of real-application UI designs, without the need for labeling relevant UI designs. We implement our approach for Android UI design search, and conduct extensive experiments with artificially created relevant UI designs and human evaluation of UI design search results. Our experiments confirm the superior performance of our search engine over existing image-similarity or component-matching-based methods and demonstrate the usefulness of our search engine in real-world UI design tasks.

Accomplishing a program task usually involves performing multiple activities in a logical order. Task-solving activities may have different relationships, such as subactivityof, precede-follow, and different attributes, such as location, condition, API, code. We refer to task-solving activities and their relationships and attributes as know-how knowledge. Programming task know-how knowledge is commonly documented in semi-structured textual tutorials. A formative study of the 20 top-viewed Android-tagged how-to questions on Stack Overflow suggests that developers are faced with three information barriers (incoherent modeling of task intent, tutorial information overload and unstructured task activity description) for effectively discovering and understanding task-solving knowledge in textual tutorials. Knowledge graph has been shown to be effective in representing relational knowledge and supporting knowledge search in a structured way. Unfortunately, existing knowledge graphs extract only know-what information (e.g., APIs, API caveats and API dependencies) from software documentation. In this paper, we devise open information extraction (OpenIE) techniques to extract candidates for task activities, activity attributes and activity relationships from programming task tutorials. The resulting knowledge graph, TaskKG, includes a hierarchical taxonomy of activities, three types of activities relationships and five types of activity attributes, and enables activity-centric knowledge search. As a proof-of-concept, we apply our approach to Android Developer Guide. A comprehensive evaluation of TaskKG shows high accuracy of our OpenIE techniques. A user study shows that TaskKG is promising in helping developers finding correct answers to programming how-to questions.

Online communities like Dribbble and GraphicBurger allow GUI designers to share their design artwork and learn from each other. These design sharing platforms are important sources for design inspiration, but our survey with GUI designers suggests additional information needs unmet by existing design sharing platforms. First, designers need to see the practical use of certain GUI designs in real applications, rather than just artworks. Second, designers want to see not only the overall designs but also the detailed design of the GUI components. Third, designers need advanced GUI design search abilities (e.g., multi-facets search) and knowledge discovery support (e.g., demographic investigation, cross-company design comparison). This paper presents Gallery D.C. http://mui-collection.herokuapp.com/, a gallery of GUI design components that harness GUI designs crawled from millions of real-world applications using reverse-engineering and computer vision techniques. Through a process of invisible crowdsourcing, Gallery D.C. supports novel ways for designers to collect, analyze, search, summarize and compare GUI designs on a massive scale. We quantitatively evaluate the quality of Gallery D.C. and demonstrate that Gallery D.C. offers additional support for design sharing and knowledge discovery beyond existing platforms.

When performing feature location tasks, developers often need to explore a large number of program elements by following a variety of clues (such as program element location, dependency, and content). As there are often complex relationships among program elements, it is likely that some relevant program elements are omitted, especially when the implementations for a feature or concern scatter across several source files. In this paper, we propose an approach for recommending potentially relevant program elements in an interactive feature location process. The two characteristics of our approach are: considering ongoing user context (i.e., confirmed or negated elements) in an interactive manner; performing an example-based reasoning to determine relevance of program elements. Based on an initial set of program elements confirmed by developers, our approach recommends additional program elements in an iterative process, in which developers can confirm relevant results, negate irrelevant results, and obtain an updated recommendation list. We have implemented our approach as an Eclipse plug-in called RecFL and conducted an experimental study. The results show that the participants using RecFL achieved a much better performance in their feature location tasks than the participants not using RecFL. The participants using RecFL also felt it easier to accomplish their feature location tasks with the support of RecFL.

Feature location is a human-oriented and information-intensive process. When performing feature location tasks with existing tools, developers often feel it difficult to formulate an accurate feature query (e.g., keywords) and determine the relevance of returned results. In this paper, we propose a feature location approach that supports multi-faceted interactive program exploration. Our approach automatically extracts and mines multiple syntactic and semantic facets from candidate program elements. Furthermore, it allows developers to interactively group, sort, and filter feature location results in a centralized, multi-faceted, and intelligent search User Interface (UI). We have implemented our approach as a web-based tool MFIE and conducted an experimental study. The results show that the developers using MFIE can accomplish their feature location tasks 32% faster and the quality of their feature location results (in terms of F-measure) is 51% higher than that of the developers using regular Eclipse IDE.

SUMMARY Developers often have to locate the parts of source code that contribute to a specific feature during software maintenance tasks. This activity, referred to as feature location in software engineering, is a human‐intensive and knowledge‐intensive process. Researchers have investigated (semi‐)automatic analysis‐based techniques to assist developers in such feature location activities. However, little work has been carried out on better understanding how developers perform feature location tasks. In this paper, we report an exploratory study of feature location process, consisting of three experiments in which developers were given unfamiliar systems and asked to complete six feature location tasks. Our study suggests that feature location process can be understood hierarchically at three levels of granularity: phase, pattern, and action. Furthermore, our statistical analysis shows that these feature location phases, patterns, and actions can be effectively imparted to junior developers and consequently improve their performance on feature location tasks. Our qualitative observations and interviews also suggest that external factors, for example, human factors, task properties, and in‐process feedbacks, affect the choices and usage of different feature location patterns and actions. Our results open up new opportunities to feature location research, which could lead to better tool support and more rigorous feature location process. Copyright © 2013 John Wiley & Sons, Ltd.

Developers often have to locate the parts of the source code that contribute to a specific feature during software maintenance tasks. This activity, referred to as feature location in software engineering, is a human- and knowledge-intensive process. Researchers have investigated information retrieval, static/dynamic analysis based techniques to assist developers in such feature location activities. However, little work has been done on better understanding how developers perform feature location tasks. In this paper, we report an exploratory study of feature location process, consisting of two experiments in which developers were given unfamiliar systems and asked to complete six feature location tasks in two hours. Our study suggests that feature location process can be understood hierarchically at three levels of granularities: phase, pattern, and action. Furthermore, our study suggests that these feature-location phases, patterns and actions can be effectively imparted to junior developers and consequently improve their performance on feature location tasks. Our results open up new opportunities to feature location research, which could lead to better tool support and more rigorous feature location process.