September 2011
Intermediate to advanced
612 pages
15h 48m
Japanese
Content preview from Making Software ―エビデンスが変えるソフトウェア開発
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398
23
章 エビデンスに基づくエラー予測
パラメタ、（
c
）クラスのメンバー関数本体中でローカルに定義されているクラス、（
d
）直 接 の
親クラスを通じての結合、（
e
）戻り値の型を通じての結合。
サブクラス数
このメトリックは、モジュールの中の与えられた親クラスから直接継承しているクラス数です。
これらのメトリクスを使ったエラー予測の全体的効果は、
Windows Vista
では精度
79.3%
、再現率
66%
でした。この測定結果からは、複雑度の基準はコードの品質について適正な予測因子ではあるが、
コード変更量ほどには良くない、ということになります。関連研究において
Khoshgoftaar
らは、（
171
モジュールで
38000
個の手続きを含む）巨大な遠距離通信用レガシーシステムの
2
つの連続したリリー
ス版を調べて、
16
の静的なソフトウェア製品メトリクスに基づいてエラーが出ると予測されるモジュー
ルを選別しました［
Khoshgoftaar et al. 1996
］。彼らのモデルを
2
つめのリリース版に適用した結果、
全体を通じて間違って分類した割合は
21.0%
でした。
El Emam
らは、巨大な遠距離通信用アプリケー
ションを使って、エラー発生傾向に関するクラスサイズの効果を調べました［
El Emam et al. 2001
］。
クラスサイズは、エラー発生傾向に関してすべての他のメトリクスよりも優れていました。
CK
メトリ
クスも、エラーの出やすさ欠陥傾向の文脈で調査されました。
Basili
らは、
8
人の学生プロジェクトを使っ ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.