Test smells, suboptimal patterns in test code, impair software maintainability and reliability, especially in resourceconstrained open-source Python projects. While detection tools such as PyNose identify python-specific test smells, prioritizing them for refactoring remains a challenge due to the lack of test-specific frameworks. This study proposes a metric-driven approach that integrates Change Proneness (CP) and Fault Proneness (FP) metrics, computed via Spearman's rank correlation, to quantify maintenance and reliability risks across 15 test smells in 52 open-source Python projects. Complementing this, a survey of$\mathbf{4 5}$developers captures subjective severity perceptions. By applying Martin Fowler's Technical Debt Quadrant, we classify smells based on empirical risk and developer insights into four categories, enabling better prioritization. Out of the 15 analyzed smells, Conditional Test Logic, Duplicate Assert, Obscure In-Line Setup, and Redundant Assertion belong to the highestpriority category for refactoring. These smells are characterized by both high empirical risk and strong developer agreement. This integrated framework advances test smell prioritization by combining data-driven analysis with practitioner perspectives, facilitating efficient refactoring decisions and improved test suite quality.

Software vulnerabilities are flaws in a product that compromise system security. In large software systems, developers struggle to find particular vulnerable statements from vulnerable functions when new vulnerabilities arise. Existing research underutilizes the potential of vulnerability reports which provides vital context for identifying vulnerable functions and their corresponding statements in source code. The paper introduces VFSDetector, an information retrieval-based approach for Vulnerable Functions and Statements Detection in source code using vulnerability reports. It adapts the Vector Space Model to compare vulnerability report text with source code. Initial evaluation on 10 reports from seven open-source projects show VFSDetector accurately identifies the genuine vulnerable function as the 1st function in 40% of cases and particular vulnerable statements in 20% of cases. It ranks the actual vulnerable function within the top five in 90% of cases and vulnerable statements in the top fifteen in 70% of cases. These findings can assist developers in patching vulnerable statements more quickly.

Fault-proneness is an indication of programming errors that decreases software quality and maintainability. On the contrary, code smell is a symptom of potential design problems which has impact on fault-proneness. In the literature, negative impact of code smells on fault-proneness has been investigated. However, it is still unclear that how frequency of each code smell type impacts the fault-proneness. To mitigate this research gap, we present an empirical study to identify whether frequency of individual code smell types has a relationship with the fault-proneness. The results show that Anti Singleton, Blob and Class Data Should Be Private smell types have strong relationship with fault-proneness though their frequencies are not very high. On the other hand, comparatively high frequent code smell types such as Complex Class, Large Class and Long Parameter List have moderate relationship with fault-proneness. These findings will assist developers to prioritize and refactor code smells to improve software quality.