Treffer: An Automated Tool for Streamlining Software Engineering: Information Extraction and Decision.
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In the always-evolving and dynamic field of software development, good decision-making is absolutely critical. Developers have to regularly decide how best to apply features, optimize performance and debug issues. This process could be much improved by extracting actionable insights from software code. The presented work explores the tools and metrics available to enable developers to make data-driven decisions, therefore enhancing the development efficiency as well as code quality. Also, it introduces a new automated tool called CodeLens which analyzes software code, extract lines of code (LOC), documentation quality, complexity, and other key criteria. Through a consolidated view of such metrics, the tool helps developers evaluate code fit, spot possible bottlenecks, and prioritize optimization or refactoring efforts. Furthermore, the tool's support of Java and Python languages guarantees general applicability, hence fitting for many software projects. [ABSTRACT FROM AUTHOR]
المقال يركز على تطوير وتطبيق أداة آلية تُدعى CodeLens، مصممة لتعزيز اتخاذ القرارات في تطوير البرمجيات من خلال استخراج رؤى قابلة للتنفيذ من كود البرمجيات. تقوم CodeLens بتحليل مقاييس رئيسية مثل عدد الأسطر في الكود، وجودة الوثائق، وتعقيد الكود، مما يوفر للمطورين رؤية موحدة لحالة كودهم. تدعم الأداة لغتي Java وPython، مما يجعلها قابلة للتطبيق عبر مشاريع برمجية متنوعة. من خلال دراسات حالة تتضمن مستودعات مفتوحة المصدر الشهيرة، يتم توضيح فعالية CodeLens في تحديد مشكلات جودة الكود وتسهيل تخصيص الموارد بشكل أفضل، مما يبرز إمكانياتها في تحسين قابلية صيانة البرمجيات وأدائها. [Extracted from the article]
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