Background: Given information on just a few prior projects, how do we learn the best and fewest changes for current projects? Aim: To conduct a case study comparing two ways to recommend project changes. 1) Data farmers use Monte Carlo sampling to survey and summarize the space of possible outcomes. 2) Case-based reasoners (CBR) explore the neighborhood around test instances. Method: We applied a state-of-the data farmer (SEESAW) and a CBR tool ()'V2) to software project data. Results: CBR with )'V2 was more effective than SEESAW's data farming for learning best and recommended project changes, effectively reducing runtime, effort, and defects. Further, CBR with )'V2 was comparably easier to build, maintain, and apply in novel domains, especially on noisy data sets. Conclusion: Use CBR tools like )'V2 when data are scarce or noisy or when project data cannot be expressed in the required form of a data farmer. Future Work: This study applied our own CBR tool to several small data sets. Future work could apply other CBR tools and data farmers to other data (perhaps to explore other goals such as, say, minimizing maintenance effort).

Often repositories of systems engineering artifacts at NASA's Jet Propulsion Laboratory (JPL) are so large and poorly structured that they have outgrown our capability to effectively manually process their contents to extract useful information. Sophisticated text mining methods and tools seem a quick, low-effort approach to automating our limited manual efforts. Our experiences of exploring such methods mainly in three areas including historical risk analysis, defect identification based on requirements analysis, and over-time analysis of system anomalies at JPL, have shown that obtaining useful results requires substantial unanticipated efforts - from preprocessing the data to transforming the output for practical applications. We have not observed any quick 'wins' or realized benefit from short-term effort avoidance through automation in this area. Surprisingly we have realized a number of unexpected long-term benefits from the process of applying text mining to our repositories. This paper elaborates some of these benefits and our important lessons learned from the process of preparing and applying text mining to large unstructured system artifacts at JPL aiming to benefit future TM applications in similar problem domains and also in hope for being extended to broader areas of applications.

Before performing drastic changes to a project, it is worthwhile to thoroughly explore the available options within the current structure of a project. An alternative to drastic change are internal changes that adjust current options within a software project. In this paper, we show that the effects of numerous internal changes can out-weigh the effects of drastic changes. That is, the benefits of drastic change can often be achieved without disrupting a project. The key to our technique is SEESAW, a novel stochastic stability tool that (a) considers a very large set of minor changes using stochastic sampling; and (b) carefully selects the right combination of effective minor changes. Our results show, using SEESAW, project managers have more project improvement options than they currently realize. This result should be welcome news to managers struggling to maintain control and continuity over their project in the face of multiple demands for drastic change.

Adoption of advanced automated SE (ASE) tools would be favored if a business case could be made that these tools are more valuable than alternate methods. In theory, software prediction models can be used to make that case. In practice, this is complicated by the "local tuning" problem. Normally, predictors for software effort and defects and threat use local data to tune their predictions. Such local tuning data is often unavailable.

Effort estimation often requires generalizing from a small number of historical projects. Generalization from such limited experience is an inherently underconstrained problem. Hence, the learned effort models can exhibit large deviations that prevent standard statistical methods (e.g., t-tests) from distinguishing the performance of alternative effort-estimation methods. The COSEEKMO effort-modeling workbench applies a set of heuristic rejection rules to comparatively assess results from alternative models. Using these rules, and despite the presence of large deviations, COSEEKMO can rank alternative methods for generating effort models. Based on our experiments with COSEEKMO, we advise a new view on supposed "best practices" in model-based effort estimation: 1) Each such practice should be viewed as a candidate technique which may or may not be useful in a particular domain, and 2) tools like COSEEKMO should be used to help analysts explore and select the best method for a particular domain

COCONUT calibrates effort estimation models using an ex-haustive search over the space of calibration parameters in a COCOMO I model. This technique is much simpler than other effort estimation method yet yields PRED levels com-parable to those other methods. Also, it does so with less project data and fewer attributes (no scale factors). How-ever, a comparison between COCONUT and other methods is complicated by differences in the experimental methods used for effort estimation. A review of those experimental methods concludes that software effort estimation models should be calibrated to local data using incremental hold-out (not jack knife) studies, combined with randomization and hypothesis testing, repeated a statistically significant number of times.

Despite the widespread availability of software effort estimation models (e.g. COCOMO [2], Price-S [12], SEER-SEM [13], SLIM [14]), most managers still estimate new projects by extrapolating from old projects [3, 5, 7]. In this delta method, the cost of the next project is the cost of the last project multiplied by some factors modeling the difference between old and new projects [2].Delta estimation is simple, fast, and best of all, can take full advantage of local costing information. However delta estimation fails when the experience base (the old projects) can not be extrapolated to the new projects. Previously [10], we have shown that for a set of NASA projects, delta estimation would usually fail since most of the features and coefficients of the learned model vary wildly across sub-samples of the training data. In that prior work, no solution was offered for this problem.Here, we offer a solution and report the results of experiment with feature subset selection (FSS) and extrapolation. FSS methods are usually assessed via the mean change in model performance. However, as shown below, FSS can significantly reduce the variance as well. Hence, FSS should be routinely used in cost estimation.Our results should stop the trend in the effort modeling community of continually adding to the number of features in a model in order to improve estimation performance. Here we show that there are benefits in intelligently subtracting model features.