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Register a new userConE: A Concurrent Edit Detection Tool for Large ScaleSoftware Development
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Added by
Chandra Maddila
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Modern, complex software systems are being continuously extended and adjusted. The developers responsible for this may come from different teams or organizations, and may be distributed over the world. This may make it difficult to keep track of what other developers are doing, which may result in multiple developers concurrently editing the same code areas. This, in turn, may lead to hard-to-merge changes or even merge conflicts, logical bugs that are difficult to detect, duplication of work, and wasted developer productivity. To address this, we explore the extent of this problem in the pull request based software development model. We study half a year of changes made to six large repositories in Microsoft in which at least 1,000 pull requests are created each month. We find that files concurrently edited in different pull requests are more likely to introduce bugs. Motivated by these findings, we design, implement, and deploy a service named ConE (Concurrent Edit Detector) that proactively detects pull requests containing concurrent edits, to help mitigate the problems caused by them. ConE has been designed to scale, and to minimize false alarms while still flagging relevant concurrently edited files. Key concepts of ConE include the detection of the Extent of Overlap between pull requests, and the identification of Rarely Concurrently Edited Files. To evaluate ConE, we report on its operational deployment on 234 repositories inside Microsoft. ConE assessed 26,000 pull requests and made 775 recommendations about conflicting changes, which were rated as useful in over 70% (554) of the cases. From interviews with 48 users we learned that they believed ConE would save time in conflict resolution and avoiding duplicate work, and that over 90% intend to keep using the service on a daily basis.
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Automated compilation error repair, the problem of suggesting fixes to buggy programs that fail to compile, has generated significant interest in recent years. Apart from being a tool of general convenience, automated code repair has significant pedagogical applications for novice programmers who find compiler error messages cryptic and unhelpful. Existing approaches largely solve this problem using a blackbox-application of a heavy-duty generative learning technique, such as sequence-to-sequence prediction (TRACER) or reinforcement learning (RLAssist). Although convenient, such black-box application of learning techniques makes existing approaches bulky in terms of training time, as well as inefficient at targeting specific error types. We present MACER, a novel technique for accelerated error repair based on a modular segregation of the repair process into repair identification and repair application. MACER uses powerful yet inexpensive discriminative learning techniques such as multi-label classifiers and rankers to first identify the type of repair required and then apply the suggested repair. Experiments indicate that the fine-grained approach adopted by MACER offers not only superior error correction, but also much faster training and prediction. On a benchmark dataset of 4K buggy programs collected from actual student submissions, MACER outperforms existing methods by 20% at suggesting fixes for popular errors that exactly match the fix desired by the student. MACER is also competitive or better than existing methods at all error types -- whether popular or rare. MACER offers a training time speedup of 2x over TRACER and 800x over RLAssist, and a test time speedup of 2-4x over both.
This volume contains the proceedings of F-IDE 2019, the fifth international workshop on Formal Integrated Development Environment, which was held on October 7, 2019 in Porto, Portugal, as part of FM19, the 3rd World Congress on Formal Methods. High levels of safety, security and privacy standards require the use of formal methods to specify and develop compliant software (sub)systems. Any standard comes with an assessment process, which requires a complete documentation of the application in order to ease the justification of design choices and the review of code and proofs. Thus tools are needed for handling specifications, program constructs and verification artifacts. The aim of the F-IDE workshop is to provide a forum for presenting and discussing research efforts as well as experience returns on design, development and usage of formal IDE aiming at making formal methods easier for both specialists and non-specialists.
This volume contains the proceedings of F-IDE 2021, the sixth international workshop on Formal Integrated Development Environment, which was held online on May 24-25, 2021, as part of NFM21, the 13th NASA Formal Methods Symposium. High levels of safety, security and privacy standards require the use of formal methods to specify and develop compliant software (sub)systems. Any standard comes with an assessment process, which requires a complete documentation of the application in order to ease the justification of design choices and the review of code and proofs. Thus tools are needed for handling specifications, program constructs and verification artifacts. The aim of the F-IDE workshop is to provide a forum for presenting and discussing research efforts as well as experience returns on design, development and usage of formal IDE aiming at making formal methods more accessible for both specialists and non-specialists.
Database Management System (DBMS) plays a core role in modern software from mobile apps to online banking. It is critical that DBMS should provide correct data to all applications. When the DBMS returns incorrect data, a correctness bug is triggered. Current production-level DBMSs still suffer from insufficient testing due to the limited hand-written test cases. Recently several works proposed to automatically generate many test cases with query transformation, a process of generating an equivalent query pair and testing a DBMS by checking whether the system returns the same result set for both queries. However, all of them still heavily rely on manual work to provide a transformation which largely confines their exploration of the valid input query space. This paper introduces duplicate-sensitivity guided transformation synthesis which automatically finds new transformations by first synthesizing many candidates then filtering the nonequivalent ones. Our automated synthesis is achieved by mutating a query while keeping its duplicate sensitivity, which is a necessary condition for query equivalence. After candidate synthesis, we keep the mutant query which is equivalent to the given one by using a query equivalent checker. Furthermore, we have implemented our idea in a tool Eqsql and used it to test the production-level DBMSs. In two months, we detected in total 30 newly confirmed and unique bugs in MySQL, TiDB and CynosDB.
Ethereum has become a widely used platform to enable secure, Blockchain-based financial and business transactions. However, a major concern in Ethereum is the security of its smart contracts. Many identified bugs and vulnerabilities in smart contracts not only present challenges to maintenance of blockchain, but also lead to serious financial loses. There is a significant need to better assist developers in checking smart contracts and ensuring their reliability.In this paper, we propose a web service tool, named SmartEmbed, which can help Solidity developers to find repetitive contract code and clone-related bugs in smart contracts. Our tool is based on code embeddings and similarity checking techniques. By comparing the similarities among the code embedding vectors for existing solidity code in the Ethereum blockchain and known bugs, we are able to efficiently identify code clones and clone-related bugs for any solidity code given by users, which can help to improve the users confidence in the reliability of their code. In addition to the uses by individual developers, SmartEmbed can also be applied to studies of smart contracts in a large scale. When applied to more than 22K solidity contracts collected from the Ethereum blockchain, we found that the clone ratio of solidity code is close to 90%, much higher than traditional software, and 194 clone-related bugs can be identified efficiently and accurately based on our small bug database with a precision of 96%. SmartEmbed can be accessed at url{http://www.smartembed.net}. A demo video of SmartEmbed is at url{https://youtu.be/o9ylyOpYFq8}
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