Fault prediction is a promising technique that can potentially help developers build software with fewer faults. Bugspots is an algorithm developed by Google, that is used for its simplicity and short run times and is used as a baseline for other fault prediction algorithms. Linespots is a variant of Bugspots that works on lines instead of files, thus potentially improving performance through higher precision. In this thesis, we analyzed the effect different weighting-functions and age-calculations have on the performance of Linespots, investigated the possibility to turn Linespots into a classifier and compared the performance and results of Linespots to those of Bugspots. Based on the algorithms, weighting-functions and age-calculations, we used a full factorial experiment design where we evaluated a total of 65 revisions of 23 open source projects from GitHub and analyzed the resulting 780 samples using Bayesian data analysis. We found that none of the weighting-functions or age-calculation variants had any reliable effect on the performance of Linespots and that the classification performance of Linespots makes it unsuited for production use. Furthermore, we found that while the ranked result lists differ between Bugspots and Linespots, the averaged predictive performance is similar. However, Linespots tends to outperform Bugspots for the early parts of the result list. These findings implicate that Linespots could be a better baseline choice for fault prediction than Bugspots, but there is more work needed to identify the optimal parameters for Linespots. Moreover, additional investigations are needed into interactions between different parameters and both the weighting-function and age-calculations, as well as the methodology of using a pseudo future for evaluation.