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Cost estimation is one of the most important but most difficult tasks in software project management. Many methods have been proposed for software cost estimation. Analogy Based Estimation (ABE), which is essentially a case-based reasoning (CBR) approach, is one popular technique. To improve the accuracy of ABE method, several studies have been focusing on the adjustments to the original solutions. However, most published adjustment mechanisms are based on linear forms and are restricted to numerical type of project features. On the other hand, software project datasets often exhibit nonnormal characteristics with large proportions of categorical features. To explore the possibilities for a better adjustment mechanism, this paper proposes Artificial Neural Network (ANN) for Non-linear adjustment to ABE (NABE) with the learning ability to approximate complex relationships and incorporating the categorical features. The proposed NABE is validated on four real world datasets and compared against the linear adjusted ABEs, CART, ANN and SWR. Subsequently, eight artificial datasets are generated for a systematic investigation on the relationship between model accuracies and dataset properties. The comparisons and analysis show that non-linear adjustment could generally extend ABE's flexibility on complex datasets with large number of categorical features and improve the accuracies of adjustment techniques.

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 There have been many reported successful cases of Six Sigma implementation in the past few years. As stock price performance is one of the realistic criteria for business performance, two studies are presented in this paper in this regard. One is stock prices' reaction on the day when Six Sigma activities were made known publicly, and the other is the long-run stock performance of 'Six Sigma companies'. The abnormal returns on the event day are evaluated in terms of three models, namely mean adjusted, market adjusted and market models. A full sample consists of 20 announcements, and two sub-samples with four announcements and 16 announcements each were analysed. The result shows that the abnormal returns are not significant on the event day. A study on six firms shows that, in the long-run, stock performance of Six Sigma companies did not significantly outperform S&P 500. This analysis of stock price performance would help set a realistic expectation of Six Sigma benefits. It offers an alternative perspective on the impact of Six Sigma on a macro scale, rather than the common project-by-project performance measures such as defect minimization and immediate cost savings.

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