Detecting code clones is an established method for comprehending and maintaining systems. One important but challenging form of code clone detection involves detecting semantic clones, which are those that are semantically similar code segments that differ syntactically. Existing approaches to semantic clone detection do not scale well to large code bases and have room for improvement in their precision and recall. In this paper, we present a scalable slicing-based approach for detecting code clones, including semantic clones. We determine code segment similarity based on their corresponding program slices. We take advantage of a lightweight, publicly available, and scalable program slicing approach to compute the necessary information. Our approach uses dependency analysis to find and measure cloned elements, and provides insights into elements of the code that are affected by an entire clone set/- class. We have implemented our approach as a tool called srcClone. We evaluate it by comparing it to two semantic clone detectors in terms of clones, performance, and scalability; and perform recall and precision analysis using established benchmark scenarios. In our evaluation, we illustrate our approach is both relatively scalable and accurate. srcClone can also be used by program analysts to run on non-compilable and incomplete source code, which serves comprehension and maintenance tasks very well. We believe our approach is an important advancement in program comprehension that can help improve clone detection practices and provide developers greater insights into their software.