1a shows a non-buggy code snippet with two while loops. However, the handcrafted features of clean and buggy code can resemble, which will make it hard for classifiers to distinguish the difference.įor example, Fig. Handcrafted features mainly focus on the statistical characteristics of the program, assuming that the buggy code has distinguishable features when compared to the clean ones. And many studies have proven the success usage of AST. The former approach used handcrafted features like Halstead features, McCabe features, CK features, and build classifiers using machine learning techniques such as logistic regression (LR) or support vector machine, while the latter used deep learning techniques to construct powerful neural networks and made use of the program's Abstract Syntax Trees (ASTs) to build prediction models. Previous studies on SDP mainly focussed on using either machine learning or deep learning techniques to build an effective defect prediction model. SDP is a process of building a defect prediction model using the historical data and then predict where the new code is defective. And software defect prediction has attracted many researchers in recent years. Therefore, software defect prediction (SDP) has been proposed not only to reduce the cost and time for software testing, but also help the assurance team to locate the defective code more easily. However, to find as many bugs as possible, software testing often requires a large amount of time to perform various test cases, thus, with finite budgets and tight schedule, it is impractical to run the test for the entire project. To ensure software quality, companies have to employ quality assurance team to find defects in the software, which is a labour-intensive and costly work. Modern software is becoming more and more powerful, thus its scale and complexity continue to increase, which brought a great threat to the quality and reliability of software. Experimental results on several opensource projects showed that the proposed LSTM method is superior to the state-of-the-art methods. They traverse the AST of each file and fed them into the LSTM network to automatically the semantic and contextual features of the program, which is then used to determine whether the file is defective. Specifically, they first extract the program's Abstract Syntax Trees (ASTs), which is made up of AST nodes, and then evaluate what and how much information they can preserve for several node types. In this study, the authors leveraged a long short-term memory (LSTM) network to automatically learn the semantic and contextual features from the source code. Failing to catch this significant information, the performance of traditional approaches is far from satisfactory. However, like human languages, programming languages contain rich semantic and structural information, and the cause of defective code is closely related to its context. Traditional software defect prediction approaches mainly focused on using hand-crafted features to detect defects. Software quality plays an important role in the software lifecycle. IET Generation, Transmission & Distribution.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.But starting it from the commandline will launch as many instances as many times you executed the command in Terminal. if you start jEdit as an app., it'll launch only a single running instance, regardless of how many times you try to start it. It'll behave a bit differently as a normal application used to. One more thing: if you start jEdit like I told you to do, it'll launch an "out of application" instance. since they made the 64-bit readiness one of the major points in their Snow Leopard marketing campaign. I've not yet met Snow Leopard face-to-face, but I could imagine that Apple already deploys a 64 bit JVM on that platform. In case of Mac OS X Leopard (10.5.x) the JVM runs in 32 bit mode and lets you set a maximum heap size somewhere between 1.5GB and 2GB (even if you've several times that much RAM in you Mac). You can set it to any value that your architecture (RAM and JVM) allows. The -Xmx192M parameter determines the Java heap size. Java -cp /Applications/jEdit.app/Contents/Resources/Java/jedit.jar:/System/Library/Java -Xmx192M =true =true =true .jedit.jEdit -background
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