#Text deduplicator java app code

We have implemented CHAMELEON on top of a IBM's J9 production JVM, and evaluated it over a small set of benchmarks. The tool can apply these corrective strategies automatically or present them to the programmer.

These metrics are consumed on-thefly by a rules engine which outputs a list of suggested collection adaptation strategies. During program execution, CHAMELEON computes elaborate trace and heap-based metrics on collection behavior. In this paper, we present CHAMELEON, a low-overhead automatic tool that assists the programmer in choosing the appropriate collection implementation for her application. Using the collection in a manner not consistent with this fixed choice can cause significant performance degradation. A collection implementation represents a fixed choice in the dimensions of operation time, space utilization, and synchronization. Languages such as Java and C#, as well as scripting languages like Python, and Ruby, make extensive use of Collection classes. Cork is accurate, scalable, and efficient enough to consider using online. Cork's slice report enabled us (non-experts) to quickly eliminate growing data structures in SPECjbb2000 and Elipse, something their developers had not previously done. Cork is accurate: it identifies systematic heap growth with no false positives in 4 of 15 benchmarks we tested. Cork compares TPFGs across multiple collections, identifies growing data structures, and computes a type slice for the user. We implement this approach in Cork which utilizes dynamic type information and garbage collection to summarize the live objects in a type points-from graph (TPFG) whose nodes (types) and edges (references between types) are annotated with volume. These overheads preclude their use on the same processor for deployed long-running applications.This paper introduces a dynamic heap-summarization technique based on type that accurately identifies leaks, is space efficient (adding less than 1% to the heap), and is time efficient (adding 2.3% on average to total execution time). Prior approaches for detecting memory leaks rely on heap differencing or detailed object statistics which store state proportional to the number of objects in the heap. Memory leaks cause systematic heap growth, degrading performance and resulting in program crashes after perhaps days or weeks of execution. Our case studies show that our framework can detect new and known real-world performance bugs and that fixing the detected performance anti- patterns can improve the system response time by up to 98%.ДЄ memory leak in a garbage-collected program occurs when the program inadvertently maintains references to objects that it no longer needs. We have successfully evaluated our framework on two open source and one large-scale industrial systems. Furthermore, as there could be hundreds or even thousands of instances of anti-patterns, our framework provides sup- port to prioritize performance bug fixes based on a statistically rigorous performance assessment. Our framework automatically flags performance anti-patterns in the source code. In this paper, we propose an automated framework to detect ORM performance anti-patterns. Unfortunately, there is little support to help developers automatically detect suboptimal database accesses.

#Text deduplicator java app code

However, developers often write ORM code without considering the impact of such code on database performance, leading to cause transactions with timeouts or hangs in large-scale systems. ORM is widely used in industry due to its convenience permitting developers to focus on developing the business logic without worrying too much about the database access details. Object-Relational Mapping (ORM) provides developers a conceptual abstraction for mapping the application code to the underlying databases.