Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique used to build code search tools, and classified existing tools into focusing on supporting seven different search tasks. Based on our findings, we identified a set of outstanding challenges in existing studies and a research roadmap for future code search research.

Opportunities and Challenges in Code Search Tools

The overexpansion problem negatively affects the quality of query expansion. To improve the quality of queries for searching code, this paper proposed a DBN‐based algorithm for effective query expansion. The deep belief network (DBN) model is trained on the code sequences and their change sequences, which aims to capture the meaningful terms during the evolution of source code. In contrast to previous studies, the proposed model not only extracts relevant terms to expand a query but also excludes irrelevant terms from the query. It addresses two problems in query expansion, including the overexpansion of the original query and the negative influence of the changed terms in the target source code. Experiments on both artificial queries and real queries show that the proposed algorithm outperforms several query expansion algorithms for code search.

Deep learning the semantics of change sequences for query expansion

Software developers have heavily used online question-and-answer platforms to seek help to solve their technical problems. However, a major problem with these technical Q8A sites is “answer hungriness,” i.e., a large number of questions remain unanswered or unresolved, and users have to wait for a long time or painstakingly go through the provided answers with various levels of quality. To alleviate this time-consuming problem, we propose a novel DEEPANS neural network–based approach to identify the most relevant answer among a set of answer candidates. Our approach follows a three-stage process: question boosting, label establishment, and answer recommendation. Given a post, we first generate a clarifying question as a way of question boosting. We automatically establish the positive, neutral+, neutral-, and negative training samples via label establishment. When it comes to answer recommendation, we sort answer candidates by the matching scores calculated by our neural network–based model. To evaluate the performance of our proposed model, we conducted a large-scale evaluation on four datasets, collected from the real-world technical Q8A sites (i.e., Ask Ubuntu, Super User, Stack Overflow Python, and Stack Overflow Java). Our experimental results show that our approach significantly outperforms several state-of-the-art baselines in automatic evaluation. We also conducted a user study with 50 solved/unanswered/unresolved questions. The user-study results demonstrate that our approach is effective in solving the answer-hungry problem by recommending the most relevant answers from historical archives.

https://dl.acm.org/doi/pdf/10.1145/3412845

Technical Q8A Site Answer Recommendation via Question Boosting

The use of an automatic query expansion technique is to enhance the performance of the Information Retrieval System. Selecting the candidate terms for query expansion is an essential task to make query more precise to extract the most suitable documents. This paper provides a method to select the best terms for query enhancement. Firstly, the effect of abbreviation resolution, Lexical Variation, Synonyms, n-gram pseudo-relevance feedback, Co-occurrence method on baseline approaches of query expansion is analyzed.. In this work, we used the Okapi BM25 algorithm for ranking. We used Concept-based normalization to deal with concept terms. Here our results show the improvement in results than the baseline approach. A new combined technique that integrates lexical variation, synonyms, n-gram pseudo relevance feedback for query enhancement is proposed. For experimental purpose three English written datasets CACM, CISI, and TREC-3 is used. The obtained results show improvement in the performance of query expansion concerning mean average precision, F-measure, and precision-recall curve.

A contemporary combined approach for query expansion

Colors and visual design are the main visual languages for expressing the emotions and content of a webpage. The layout of a webpage is crucial to website aesthetics and affects user preference. A webpage’s title, image, colors, and structure affects the design style of its visual interface. This study consisted of two experiments. Experiment 1 focused on the color emotional characteristics of multimedia webpage color combinations and identified the most popular dual-tone color designs for multimedia webpages. Experiment 2 involved an examination of the optimal visual effect design for multimedia webpage composition. Ten types of geometric shapes were selected and combined with dynamic pictures, and the combined products were then used to conduct a multimedia dynamic pictorial webpage experiment; the purpose of the experiment was to test the effects of geometrical dynamic shapes on webpage psychology and determine the most popular multimedia dynamic picture visual design style. The researchers used semantic differential and Likert scales to conduct psychological tests aimed at measuring psychological reactions to various webpage colors and visual designs. The results indicated that the most popular color combinations for multimedia webpages are cyan and gray, cyan and black, and blue violet and gray. The most suitable graphic shape for multimedia visual designs is the oval and square dynamic picture composition. The results of the multimedia webpage color and visual effect design experiments can serve as a reference for webpage design-related research and to webpage designers.

Multimedia webpage visual design and color emotion test

A novel webpage layout aesthetic evaluation model for quantifying webpage layout design

Thesaurus‐based, code‐related, and software‐specific query expansion techniques are the main contributions in free‐form query search. However, these techniques still could not put the most relevant query result in the first position because they lack the ability to infer the expansion words that represent the user needs based on a given query. In this paper, we discover that code changes can imply what users want and propose a novel query expansion technique with code changes (QECC). It exploits (changes, contexts) pairs from changed methods. On the basis of statistical learning from pairs, it can infer code changes for a given query. In this way, it expands a query with code changes and recommends the query results that meet actual needs perfectly. In addition, we implement InstaRec to perform QECC and evaluate it with 195 039 change commits from GitHub and our code tracker. The results show that QECC can improve the precision of 3 code search algorithms (ie, IR, Portfolio, and VF) by up to 52% to 62% and outperform the state‐of‐the‐art query expansion techniques (ie, query expansion based on crowd knowledge and CodeHow) by 13% to 16% when the top 1 result is inspected.

Xue Zhan

Papers

A novel webpage layout aesthetic evaluation model for quantifying webpage layout design

Query expansion based on statistical learning from code changes