Software development is prone to software faults due to the involvement of multiple stakeholders especially during the fuzzy phases (requirements and design). Software inspections are commonly used in industry to detect and fix problems in requirements and design artifacts, thereby mitigating the fault propagation to later phases where the same faults are harder to find and fix. The output of an inspection process is list of faults that are present in software requirements specification document (SRS). The artifact author must manually read through the reviews and differentiate between true-faults and false-positives before fixing the faults. The first goal of this research is to automate the detection of useful vs. non-useful reviews. Next, post-inspection, requirements author has to manually extract key problematic topics from useful reviews that can be mapped to individual requirements in an SRS to identify fault-prone requirements. The second goal of this research is to automate this mapping by employing Key phrase extraction (KPE) algorithms and semantic analysis (SA) approaches to identify fault-prone requirements. During fault-fixations, the author has to manually verify the requirements that could have been impacted by a fix. The third goal of my research is to assist the authors post-inspection to handle change impact analysis (CIA) during fault fixation using NL processing with semantic analysis and mining solutions from graph theory. The selection of quality inspectors during inspections is pertinent to be able to carry out post-inspection tasks accurately. The fourth goal of this research is to identify skilled inspectors using various classification and feature selection approaches. The dissertation has led to the development of automated solution that can identify useful reviews, help identify skilled inspectors, extract most prominent topics/keyphrases from fault logs; and help RE author during the fault-fixation post inspection.