With the growing requirements of web applications, web components are developed to package the implementation of commonly-used features for reuse. In some cases, the developer may want to reuse some features which cannot be customized by the component's APIs. He/she has to extract the implementation by hand. It is labor-intensive and error-prone. Considering the widely-used test cases which can be useful to specify the software features, a test-driven approach is proposed to extract the implementation of the desired features in web components. The satisfaction of the user's requirements is transformed into the passing rate of user-specified test cases. In this way, the quality of the extraction result can be evaluated automatically. Meanwhile, a record/replay-based GUI test generation method is proposed to ensure that the extraction result has the correct GUI appearance. To extract the feature implementation, a hierarchical genetic algorithm is proposed to find the code snippet that can pass all the tests and has the approximate smallest size. We compare our method with two existing feature extraction methods. The result shows that our method can extract the correct implementation with the minimum size. A human-subject study is conducted to show the effectiveness and weaknesses of our method in helping users extract the features.

Ethereum blockchain is a new internetware with tens of millions of smart contracts running on it. Different from general programs, smart contracts are decentralized, tamper-resistant and permanently running. Moreover, to avoid resource abuse, Ethereum charges users for deploying and invoking smart contracts according to the size of contract and the operations executed by contracts. It is necessary to optimize smart contracts to save money. However, since developers are not familiar with the operating environment of smart contracts (i.e., Ethereum virtual machine) or do not pay attention to resource consumption during development, there are many optimization opportunities for smart contracts. To fill this gap, this paper defines six gas-inefficient patterns from more than 25000 posts and proposes an optimization approach at the source code level to let users know clearly where the contract is optimized. To evaluate the prevalence and economic benefits of gas-inefficient patterns, this paper conducts an empirical study on more than 160000 real smart contracts. The promising experimental results demonstrate that 52.75% of contracts contain at least one gas-inefficient pattern proposed in this paper. If these patterns are removed from the contract, at least $0.30 can be saved per contract.

Commit messages are important complementary information used in understanding code changes. To address message scarcity, some work is proposed for automatically generating commit messages. However, most of these approaches focus on generating summary of the changed software entities at the superficial level, without considering the intent behind the code changes (e.g., the existing approaches cannot generate such message: “fixing null pointer exception”). Considering developers often describe the intent behind the code change when writing the messages, we propose ChangeDoc, an approach to reuse existing messages in version control systems for automatical commit message generation. Our approach includes syntax, semantic, pre-syntax, and pre-semantic similarities. For a given commit without messages, it is able to discover its most similar past commit from a large commit repository, and recommend its message as the message of the given commit. Our repository contains half a million commits that were collected from SourceForge. We evaluate our approach on the commits from 10 projects. The results show that 21.5% of the recommended messages by ChangeDoc can be directly used without modification, and 62.8% require minor modifications. In order to evaluate the quality of the commit messages recommended by ChangeDoc, we performed two empirical studies involving a total of 40 participants (10 professional developers and 30 students). The results indicate that the recommended messages are very good approximations of the ones written by developers and often include important intent information that is not included in the messages generated by other tools.