practical experiences of cost/schedule measure through earned value management and statistical process control

Cost and schedule measures are the most important support activities for the success of a project; it provides the basis for process improvement and project management. This paper reports practical experiences on using EVM (Earned Value Manag

Estimating fixing effort and schedule based on defect injection distribution

Detecting and fixing defects are key activities in a testing process, which consume two kinds of skill sets. Unfortunately, many current leading software estimation methods, such as COCOMO II, mainly estimate the effort depending on the size of software, and allocate testing effort proportionally among various activities. Both efforts on detecting and fixing defects, are simply counted into software testing process-phase and cannot be estimated and managed satisfactorily. In fact, the activities for detecting defects and fixing them are quite different and need differently skilled people. The inadequate effort estimation leads to the difficulty of test process management. It is also the main problem which causes software project delays. In this article, we propose a method on Quantitatively Managing Testing (TestQM) process including identifying performance objectives, establishing a performance baseline, establish a process-performance model for fixing effort, and establishing a process-performance model for fixing the schedule, which supports high-level process management mentioned in Capability Maturity Model Integration (CMMI). In our method, defect injection distribution (DID) is used to derive estimation of fixing effort and schedule. The TestQM method has been successfully applied to a software organization for their quantitative management of testing process and proved to be helpful in estimating and controlling defects, effort and schedule of the testing process

an empirical study on establishing quantitative management model for testing process

Frequently, effort of defect detecting and fixing are counted into software testing activities/phase. Current leading software estimation methods, such as COCOMO II, mainly estimate the effort depending on the size of software product and all

An empirical study on establishing quantitative management model for testing process

Frequently, effort of defect detecting and fixing are counted into software testing activities/phase. Current leading software estimation methods, such as COCOMO II, mainly estimate the effort depending on the size of software product and allocate testing effort proportionally. It can not predict detecting and fixing effort accurately. In fact, testing effort is significantly influenced by the quality of other software development activities. These lead to the difficulty of the testing effort to be estimated accurately. It is a challenging issue for quantitative software process management. In this paper, we propose an empirical method to identify performance objectives, establish performance baseline and establish quantitative management model for testing process. The method has been successfully applied to a software organization for their quantitative management of testing process

Additional file 1: Table S1. of Genome-wide trait-trait dynamics correlation study dissects the gene regulation pattern in maize kernels

The permutation test for positive and negative LA thresholds. (XLSX 12 kb

Additional file 5: Table S3. of Genome-wide trait-trait dynamics correlation study dissects the gene regulation pattern in maize kernels

Quantile-based permutation thresholds for P values, LA scores and FDRs. (XLSX 10 kb

Additional file 3: Table S2. of Genome-wide trait-trait dynamics correlation study dissects the gene regulation pattern in maize kernels

The number of LAPs with expression mediated by the LA-scouting leaders (P values <1.00 × 10−5). (XLSX 844 kb

Additional file 10: Figure S6. of Genome-wide trait-trait dynamics correlation study dissects the gene regulation pattern in maize kernels

The regulatory network that includes 23 oil concentration–associated genes and their linked LA-scouting genes. The network comprises 23 nodes and 609 edges. The thickness of the lines indicates the value of the LA and each pink and green dot represents a Z and X gene, respectively. (DOCX 74 kb

Le Monde

02 juin 18691869/06/02 (N148,A10).Appartient à l’ensemble documentaire : BbLevt