اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدLuaJava - A Scripting Tool for Java
67
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Scripting languages are becoming more and more important as a tool for software development, as they provide great flexibility for rapid prototyping and for configuring componentware applications. In this paper we present LuaJava, a scripting tool for Java. LuaJava adopts Lua, a dynamically typed interpreted language, as its script language. Great emphasis is given to the transparency of the integration between the two languages, so that objects from one language can be used inside the other like native objects. The final result of this integration is a tool that allows the construction of configurable Java applications, using off-the-shelf components, in a high abstraction level.
قيم البحث
اقرأ أيضاً
Software bloat is code that is packaged in an application but is actually not used and not necessary to run the application. The presence of bloat is an issue for software security, for performance, and for maintenance. In this paper, we introduce a
novel technique to debloat Java bytecode through dynamic analysis, which we call trace-based debloat. We have developed JDBL, a tool that automates the collection of accurate execution traces and the debloating process. Given a Java project and a workload, JDBL generates a debloated version of the project that is syntactically correct and preserves the original behavior, modulo the workload. We evaluate JDBL by debloating 395 open-source Java libraries for a total 10M+ lines of code. Our results indicate that JDBL succeeds in debloating 62.2 % of the classes, and 20.5 % of the dependencies in the studied libraries. Meanwhile, we present the first experiment that assesses the quality of debloated libraries with respect to 1,066 clients of these libraries. We show that 957/1,001 (95.6 %) of the clients successfully compile, and 229/283 (80.9 %) clients can successfully run their test suite, after the drastic code removal among their libraries.
We study the evolution and impact of bloated dependencies in a single software ecosystem: Java/Maven. Bloated dependencies are third-party libraries that are packaged in the application binary but are not needed to run the application. We analyze the
history of 435 Java projects. This historical data includes 48,469 distinct dependencies, which we study across a total of 31,51
Reverse Engineering(RE) has been a fundamental task in software engineering. However, most of the traditional Java reverse engineering tools are strictly rule defined, thus are not fault-tolerant, which pose serious problem when noise and interferenc
e were introduced into the system. In this paper, we view reverse engineering as a statistical machine translation task instead of rule-based task, and propose a fault-tolerant Java decompiler based on machine translation models. Our model is based on attention-based Neural Machine Translation (NMT) and Transformer architectures. First, we measure the translation quality on both the redundant and purified datasets. Next, we evaluate the fault-tolerance(anti-noise ability) of our framework on test sets with different unit error probability (UEP). In addition, we compare the suitability of different word segmentation algorithms for decompilation task. Experimental results demonstrate that our model is more robust and fault-tolerant compared to traditional Abstract Syntax Tree (AST) based decompilers. Specifically, in terms of BLEU-4 and Word Error Rate (WER), our performance has reached 94.50% and 2.65% on the redundant test set; 92.30% and 3.48% on the purified test set.
Software debugging, and program repair are among the most time-consuming and labor-intensive tasks in software engineering that would benefit a lot from automation. In this paper, we propose a novel automated program repair approach based on CodeBERT
, which is a transformer-based neural architecture pre-trained on large corpus of source code. We fine-tune our model on the ManySStuBs4J small and large datasets to automatically generate the fix codes. The results show that our technique accurately predicts the fixed codes implemented by the developers in 19-72% of the cases, depending on the type of datasets, in less than a second per bug. We also observe that our method can generate varied-length fixes (short and long) and can fix different types of bugs, even if only a few instances of those types of bugs exist in the training dataset.
JSON is an essential file and data format in do-mains that span scientific computing, web APIs or configuration management. Its popularity has motivated significant software development effort to build multiple libraries to process JSON data. Previou
s studies focus on performance comparison among these libraries and lack a software engineering perspective.We present the first systematic analysis and comparison of the input / output behavior of 20 JSON libraries, in a single software ecosystem: Java/Maven. We assess behavior diversity by running each library against a curated set of 473 JSON files, including both well-formed and ill-formed files. The main design differences, which influence the behavior of the libraries, relate to the choice of data structure to represent JSON objects and to the encoding of numbers. We observe a remarkable behavioral diversity with ill-formed files, or corner cases such as large numbers or duplicate data. Our unique behavioral assessment of JSON libraries paves the way for a robust processing of ill-formed files, through a multi-version architecture.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
